# =========================================================================
# Generate tables and figures for the paper
# Date created: September 18, 2023
# Date updated: May 17, 2024
# Operating: MacBook Pro
# Previous File: `bloomberg_attention_v1wrds.R` + `10-X_clean_v2function.R` + `10-X_clean_v3function.R` + `Repurchase_BBAIA_merge_v1.R` >
# Files: (1) `NEWS_HEAT_READ_DMAX_CUSIP_CIK.csv` from Folder: <NEWS_HEAT_READ_DMAX_CUSIP_CIK.csv>
# (2) `ShaRep_Russell_Aug28_2023.csv` from Folder: <"~/repurchase_workspace_Aug21_2023/Aug23_2023/10-X_cleaned_Aug23_2023>
# Next File: > `repurchase_merge_v1function.R`
# ======================================================
# Notes: May 13, 2024
# (1) check why the coefficients in the first stage regression are so large.
# Notes: Oct 26, 2023
# (1) Add CAR measures
# (2) Add short interest info
# Notes: Sep 23, 2023
# (1) Need to update `fe_2sls` and transform a range of variables to the log transformed ones
# ======================================================
library(zoo)
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(psych)
##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(xtable)
library(stargazer)
##
## Please cite as:
##
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
library(lubridate)
##
## Attaching package: 'lubridate'
##
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(fixest)
## Table 1. su <- ary statistics ----
#setwd("~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/bloomberg_attention_July30_2023_back2023Sept7")
setwd("~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/tables_code_May04_2024")
list.files()
## [1] "BB_dt_Sept7_2023.rdata"
## [2] "edcdnnrfzr3ibtln.csv"
## [3] "fig2_attention_hist.png"
## [4] "fig2_attention2.png"
## [5] "fig2_attention3.png"
## [6] "fig2_hist.png"
## [7] "fig2_hist2.png"
## [8] "NEWS_HEAT_READ_DMAX_CUSIP_CIK_Sept8_2023.rdata"
## [9] "numerical_solution.R"
## [10] "oox7oponsih3x2us.csv"
## [11] "pq5ofpjastdmdijh.csv"
## [12] "rlvjvxigmhkhctli.csv"
## [13] "ShaRep_AIA_CCM_c2_Sept8_2023_BB.rdata"
## [14] "sharep_cusip_Oct14.txt"
## [15] "sharep_cusip_Oct15.txt"
## [16] "wbk93yls2qlscvgg.csv"
## [17] "writing_tables_May04_2024.html"
## [18] "writing_tables_May13_2024.html"
## [19] "writing_tables.R"
## [20] "writing_tables.spin.R"
## [21] "writing_tables.spin.Rmd"
### 1.a BBAIA data -----
load("BB_dt_Sept7_2023.rdata")
names(BB_dt)
## [1] "YM" "Ticker" "AIA" "AIAC" "ANews"
## [6] "NewsAverage" "NewsCount"
load(file = "NEWS_HEAT_READ_DMAX_CUSIP_CIK_Sept8_2023.rdata")
BB_dt.linked %>% head
## # A tibble: 6 × 16
## YM Ticker AIA AIAC ANews NewsA…¹ NewsC…² Marke…³ Market…⁴ CUSIP
## <yearmon> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Jan 2010 A NaN NaN 2.35 0.671 28.6 NaN NaN 0084…
## 2 Feb 2010 A 0.429 0.248 2.5 0.817 44.6 0.925 0.446 0084…
## 3 Mar 2010 A 0.478 0.183 2.30 0.688 38.0 0.861 0.385 0084…
## 4 Apr 2010 A 0.727 0.478 2 0.602 23.6 0.884 0.374 0084…
## 5 May 2010 A 0.476 0.0145 2.33 0.710 28.2 0.400 0.0219 0084…
## 6 Jun 2010 A 0 -0.35 2.23 0.504 30.5 0.670 0.215 0084…
## # … with 6 more variables: Exchange <chr>, Name <chr>, CIK <int>, gvkey <int>,
## # cusip0 <chr>, CONML <chr>, and abbreviated variable names ¹NewsAverage,
## # ²NewsCount, ³MarketAIA, ⁴MarketAIAC
## # ℹ Use `colnames()` to see all variable names
gather(select(BB_dt.linked %>% filter(AIA > 0), AIA, AIAC)) %>%
na.omit()
## # A tibble: 363,698 × 2
## key value
## <chr> <dbl>
## 1 AIA 0.429
## 2 AIA 0.478
## 3 AIA 0.727
## 4 AIA 0.476
## 5 AIA 1
## 6 AIA 1.09
## 7 AIA 1.86
## 8 AIA 1
## 9 AIA 1.14
## 10 AIA 1.43
## # … with 363,688 more rows
## # ℹ Use `print(n = ...)` to see more rows
### draw the AIA / AIAC histogram -----
ggplot(data = gather(select(BB_dt.linked %>% filter(AIA > 0 & AIA < 4), AIA, AIAC)) %>%
na.omit()
) +
geom_histogram(aes(x = value, fill = key),
colour = 'grey50', alpha = 0.5, position = 'identity') +
labs(fill = "Attention", x = NULL, y = NULL) + # Remove x-axis label
theme(axis.title.y = element_text(angle = 0, hjust = 0, vjust = 0.1),
axis.text = element_text(size = 20),
legend.text = element_text(size = 15),
legend.title = element_text(size = 18)) # Set y-axis label horizontally at the top
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
describe(BB_dt.linked %>%
select(AIA, AIAC, ANews, NewsAverage, NewsCount, MarketAIA, MarketAIAC) %>%
mutate(AIA = case_when(AIA < 0 ~ 0, AIA >= 0 ~ AIA),
MarketAIA = case_when(MarketAIA < 0 ~ 0, MarketAIA >= 0 ~ MarketAIA)
),
na.rm = T) %>%
select(mean, median, sd, min, max, n) %>%
mutate(n = as.integer(n)) %>%
`colnames<-`(., value = str_to_title(names(.))) %>%
xtable()
## % latex table generated in R 4.2.1 by xtable 1.8-4 package
## % Fri May 17 13:26:29 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrrrr}
## \hline
## & Mean & Median & Sd & Min & Max & N \\
## \hline
## AIA & 0.71 & 0.45 & 0.78 & 0.00 & 4.00 & 216662 \\
## AIAC & 0.21 & 0.00 & 0.58 & -0.35 & 2.15 & 216632 \\
## ANews & 1.45 & 1.40 & 0.62 & 0.00 & 4.00 & 372735 \\
## NewsAverage & 0.32 & 0.25 & 0.27 & 0.00 & 4.00 & 372735 \\
## NewsCount & 22.05 & 8.43 & 92.59 & 1.00 & 8419.14 & 372175 \\
## MarketAIA & 0.63 & 0.63 & 0.17 & 0.00 & 0.99 & 482444 \\
## MarketAIAC & 0.16 & 0.16 & 0.14 & -0.28 & 0.61 & 482444 \\
## \hline
## \end{tabular}
## \end{table}
hist(BB_dt.linked$NewsCount)
describe(BB_dt %>%
select(-YM, -Ticker) %>%
mutate(AIA = case_when(AIA < 0 ~ 0, AIA >= 0 ~ AIA)),
na.rm = T) %>%
select(mean, median, sd, min, max, n) %>%
mutate(n = as.integer(n)) %>%
`colnames<-`(., value = str_to_title(names(.))) %>%
xtable()
## % latex table generated in R 4.2.1 by xtable 1.8-4 package
## % Fri May 17 13:26:31 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrrrr}
## \hline
## & Mean & Median & Sd & Min & Max & N \\
## \hline
## AIA & 0.71 & 0.45 & 0.78 & 0.00 & 4.00 & 215477 \\
## AIAC & 0.21 & 0.00 & 0.58 & -0.35 & 2.15 & 215447 \\
## ANews & 1.45 & 1.40 & 0.62 & 0.00 & 4.00 & 370798 \\
## NewsAverage & 0.32 & 0.25 & 0.27 & 0.00 & 4.00 & 370798 \\
## NewsCount & 22.03 & 8.41 & 92.67 & 1.00 & 8419.14 & 370238 \\
## \hline
## \end{tabular}
## \end{table}
BB_dt_count <- BB_dt %>%
mutate(AIA = case_when(AIA < 0 ~ 0, AIA >= 0 ~ AIA)) %>%
select(-Ticker) %>%
group_by(YM) %>%
summarise_all(.funs = function(x) sum(!is.na(x))) %>%
ungroup() %>%
gather(key = Item, value = 'N', AIA:NewsCount)
BB_dt_sa <- BB_dt %>%
mutate(AIA = case_when(AIA < 0 ~ 0, AIA >= 0 ~ AIA)) %>%
select(-Ticker) %>%
group_by(YM) %>%
summarise_all(.funs = function(x) mean(x, na.rm = T) ) %>%
ungroup() %>%
gather(key = Item, value = 'Mean', AIA:NewsCount)
ggplot(BB_dt_count, aes(x = YM, y = N)) +
geom_line(aes(col = Item))
ggplot(BB_dt_sa, aes(x = YM, y = Mean)) +
geom_line(aes(col = Item))
## Warning: Removed 2 row(s) containing missing values (geom_path).
plotly::ggplotly(ggplot(BB_dt_count, aes(x = YM, y = N)) +
geom_line(aes(col = Item)))
plotly::ggplotly(ggplot(BB_dt_sa, aes(x = YM, y = Mean)) +
geom_line(aes(col = Item)) )
BB_dt_count %>% group_by(Item) %>% summarise(mean = mean(N, na.rm = T))
## # A tibble: 5 × 2
## Item mean
## <chr> <dbl>
## 1 AIA 1322.
## 2 AIAC 1322.
## 3 ANews 2275.
## 4 NewsAverage 2275.
## 5 NewsCount 2271.
### 1.b Repurchase Data -----
#setwd("~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/repurchase_merge_Aug29_2023_back2023Sept7")
#load("ShaRep_Russell_Sept7_2023.rdata")
#ShaRep_cleaned_table <- ShaRep_cleaned_table %>% as_tibble
#ShaRep_cleaned_table %>%
# distinct(cik)
#### import the raw data
##### share repurchase data in Russell 3000: ~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/repurchase_workspace_Aug21_2023-2/Aug23_2023/10-X_cleaned_Aug23_2023/10-X_clean_v3function.R
#load("~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/repurchase_workspace_Aug21_2023-2/Aug23_2023/10-X_cleaned_Aug23_2023/repurchase_cleaned_unit_dvalue_Aug28_2023.rdata")
#repurchase_cleaned_unit_dvalue %>% dim
#names(repurchase_cleaned_unit_dvalue)
#unique(repurchase_cleaned_unit_dvalue$cik) %>% length()
#unique(repurchase_cleaned_unit_dvalue$id) %>% length()
##### all share repurchase data from EDGAR: ~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/repurchase_workspace_Aug21_2023-2/Aug23_2023/10-X_cleaned_Aug23_2023/10-X_clean_v2bf.R
#load("~/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/repurchase_workspace_Aug21_2023-2/Aug23_2023/10-X_cleaned_Aug23_2023/repurchase_raw2_cleaned_Aug26_2023.rdata")
#repurchase_raw2_cleaned %>% dim
#unique(repurchase_raw2_cleaned$cik) %>% length()
#unique(repurchase_raw2_cleaned$id) %>% length()
##### short interest information:
short_interest <- read.csv(file = "edcdnnrfzr3ibtln.csv") %>%
as_tibble() %>%
filter(day(ymd(datadate)) < 20) %>% # choosing the 15th business day of the month
mutate(YM = as.yearmon(datadate)) %>%
select(YM, gvkey, cusip, sic, shortintadj)
## 2. full sample ----
#load("/Users/hongyixu/Library/CloudStorage/OneDrive-HandelshögskolaniStockholm/Projects_2023/Empirical_Design_July18_2023/repurchase_merge_Aug29_2023_back2023Sept7/ShaRep_AIA_CCM_c2_Sept8_2023_BB.rdata")
load("ShaRep_AIA_CCM_c2_Sept8_2023_BB.rdata")
#### try to add the number of months after the OMR [May 4, 2024]
ShaRep_program <- ShaRep_AIA_merge_illiquidity_OMR_CCM_c2 %>%
select(YM, gvkey, cusip, OMRFlag) %>%
group_by(gvkey, cusip) %>%
mutate(OMRIdentifier = cumsum(OMRFlag)) %>%
ungroup() # %>%
# summarise(num = max(OMRIdentifier) - min(OMRIdentifier) + 1)
ShaRep_programIDyes <- ShaRep_program %>% filter(OMRFlag == TRUE) %>% select(-OMRFlag) %>% rename(OMR_YM = YM)
ShaRep_programIDno <- ShaRep_program %>% filter(OMRFlag == FALSE & OMRIdentifier == 0) %>%
group_by(gvkey, cusip, OMRIdentifier) %>%
summarise(OMR_YM = min(YM)) %>%
ungroup() %>%
select(OMR_YM, gvkey, cusip, OMRIdentifier)
## `summarise()` has grouped output by 'gvkey', 'cusip'. You can override using
## the `.groups` argument.
ShaRep_programID <- rbind.data.frame(ShaRep_programIDyes, ShaRep_programIDno)
ShaRep_programmonth <- ShaRep_program %>%
left_join(ShaRep_programID, by = c("gvkey", "cusip", "OMRIdentifier") )
ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary <- ShaRep_AIA_merge_illiquidity_OMR_CCM_c2 %>%
left_join(short_interest, by = c('YM', 'gvkey', 'cusip')) %>% # append the short interest data
left_join(select(ShaRep_programmonth, YM, gvkey, cusip, OMR_YM), by = c('YM', 'gvkey', 'cusip')) %>%
# mutate(EBITDAtA = EBITDA / TA) %>%
mutate(ShaRep3 = ShaRep3 / 10^6,
ShaOut = ShaOut / 10^6,
MarCap = MarCap / 10^6,
TradeVol = TradeVol / 10^6 ,
TradeVolDollar = TradeVolDollar / 10^6,
OMRFlag = as.numeric(OMRFlag),
# BM = BM*10^6,
Amihud_monthly = Amihud_monthly,
ShaRepYes = as.numeric(ShaRepYes),
ProgramMonth = 1 + 12 * (YM - OMR_YM), # the number of months passed after the OMR announcement. (The first (announcement) month is 1 (not 0)). [May 4, 2024]
ShortInterest = shortintadj / ShaOutPrevious # short interest at 15th of the month / last month share outstanding.
) # %>%
#left_join(select(repurchase_cleaned_unit_dvalue, period_ym, cik, reporting, filing) %>%
# distinct %>%
# group_by(period_ym, cik) %>%
# filter(filing == min(filing)) %>% ungroup() %>%
# mutate(cik = as.numeric(cik)), by = c('YM' = 'period_ym', 'CIK' = 'cik') )
ShaRep_CAR_txt <- ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
mutate(ymd = as.Date(YM, frac = 1) %>% as.character ) %>%
select(CUSIP, ymd)
# write.table(x = ShaRep_CAR_txt, file = "sharep_cusip_Oct14.txt", col.names = F, row.names = F, quote = F)
#### 2.1 clean and store the CAR for 3- and 6-months ======
{ # plug "sharep_cusip_Oct14.txt" into WRDS:
sharep_car6 <- read.csv(file = "pq5ofpjastdmdijh.csv") %>%
as_tibble() %>%
mutate(YM = as.yearmon(original_da_date)) %>%
select(YM, cusip, wv_bhar_reb6 = wv_bhar_reb, wv_bhar_nreb6 = wv_bhar_nreb)
sharep_car3 <- read.csv(file = "wbk93yls2qlscvgg.csv") %>%
as_tibble() %>%
mutate(YM = as.yearmon(original_da_date)) %>%
select(YM, cusip, wv_bhar_reb3 = wv_bhar_reb, wv_bhar_nreb3 = wv_bhar_nreb)
sharep_car1 <- read.csv(file = "oox7oponsih3x2us.csv") %>%
as_tibble() %>%
mutate(YM = as.yearmon(original_da_date)) %>%
select(YM, cusip, wv_bhar_reb1 = wv_bhar_reb, wv_bhar_nreb1 = wv_bhar_nreb)
# plug "sharep_cusip_Oct15.txt" into WRDS:
sharep_car0 <- read.csv(file = "rlvjvxigmhkhctli.csv") %>%
as_tibble() %>%
mutate(YM = as.yearmon(original_da_date) + 1/12) %>%
select(YM, cusip, wv_bhar_reb0 = wv_bhar_reb, wv_bhar_nreb0 = wv_bhar_nreb)
sharep_car <- sharep_car3 %>%
left_join(sharep_car6, by = c('YM', 'cusip')) %>%
left_join(sharep_car1, by = c('YM', 'cusip')) %>%
left_join(sharep_car0, by = c('YM', 'cusip')) %>%
mutate_at(vars(contains("reb")), .funs = function(x) as.numeric(gsub(pattern = "%", replacement = "", x)))
}
#### 2.2 append the CAR for 3- and 6-months ======
ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary <- ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
left_join(sharep_car, by = c('YM', 'CUSIP' = 'cusip'))
ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>% names
## [1] "YM" "AIAC" "ANews" "NewsAverage"
## [5] "NewsCount" "MarketAIA" "MarketAIAC" "CUSIP"
## [9] "CIK" "gvkey" "Name" "cusip8"
## [13] "CUSIP_tts" "AIA" "ShaRep3" "missing3"
## [17] "Price2" "Amihud_monthly" "OMRFlag" "Ticker"
## [21] "RET_m1" "Volatility" "AvePrice" "TradeVol"
## [25] "TradeVolDollar" "MarCap" "ShaOut" "ShaOutPrevious"
## [29] "datadate" "tic" "cusip" "atq"
## [33] "ceqq" "cheq" "oibdpq" "pstkq"
## [37] "seqq" "txditcq" "cdvcy" "costat"
## [41] "dlcq" "dlttq" "BE" "TD"
## [45] "CtA" "TA" "Year" "Quarter"
## [49] "Month" "RepIntensity" "RepIntensity_vol" "ShaRepYes"
## [53] "BM" "DivtoAsset" "EBITDA" "Leverage"
## [57] "TradeVol_scaled" "EBITDAtA" "time" "sic"
## [61] "shortintadj" "OMR_YM" "ProgramMonth" "ShortInterest"
## [65] "wv_bhar_reb3" "wv_bhar_nreb3" "wv_bhar_reb6" "wv_bhar_nreb6"
## [69] "wv_bhar_reb1" "wv_bhar_nreb1" "wv_bhar_reb0" "wv_bhar_nreb0"
tbl1_cleaned <- tbl1 <- describe(ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
# select(-c(YM, CUSIP, CIK, gvkey, Name, cusip8, cusip, datadate, time,
# missing3, ShaRepYes, UnderPriced,
# CUSIP_tts, tic, Ticker)) %>%
select(ShaRep3, RepIntensity, RepIntensity_vol,
Amihud_monthly, BM, AvePrice, CtA, DivtoAsset, EBITDAtA, TA,
Leverage, MarCap, OMRFlag, RET_m1, TradeVol, TradeVol_scaled, Volatility, ShaRepYes
) ,
na.rm = T) %>%
select(mean, median, sd, min, max, n) %>%
mutate(n = as.integer(n)) %>%
`colnames<-`(., value = str_to_title(names(.)))
tbl1_cleaned[c("RepIntensity", "RepIntensity_vol"),1:5] <-
tbl1[c("RepIntensity", "RepIntensity_vol"),1:5] * 10^2
tbl1_cleaned[c("DivtoAsset"),1:5] * 10^2
## Mean Median Sd Min Max
## DivtoAsset 0.02696258 0 0.2164935 0 10.50881
tbl1_cleaned %>%
# format(digits = 2)
xtable()
## % latex table generated in R 4.2.1 by xtable 1.8-4 package
## % Fri May 17 13:27:13 2024
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrrrr}
## \hline
## & Mean & Median & Sd & Min & Max & N \\
## \hline
## ShaRep3 & 1.04 & 0.00 & 4.47 & 0.00 & 266.40 & 73926 \\
## RepIntensity & 0.31 & 0.00 & 0.81 & 0.00 & 44.47 & 73851 \\
## RepIntensity\_vol & 1.84 & 0.00 & 5.51 & 0.00 & 546.44 & 73926 \\
## Amihud\_monthly & 0.00 & 0.00 & 0.01 & 0.00 & 1.27 & 73926 \\
## BM & 0.48 & 0.38 & 0.53 & -12.95 & 25.01 & 73383 \\
## AvePrice & 78.28 & 50.41 & 144.88 & 0.17 & 5191.13 & 73926 \\
## CtA & 0.14 & 0.09 & 0.14 & 0.00 & 1.00 & 73383 \\
## DivtoAsset & 0.00 & 0.00 & 0.00 & 0.00 & 0.11 & 73383 \\
## EBITDAtA & 0.03 & 0.03 & 0.03 & -0.90 & 0.48 & 73383 \\
## TA & 37726.95 & 6544.53 & 156608.01 & 26.58 & 3213115.00 & 73383 \\
## Leverage & 0.39 & 0.34 & 0.25 & 0.00 & 0.99 & 73383 \\
## MarCap & 23570.90 & 6389.77 & 66815.58 & 8.39 & 2447988.71 & 73926 \\
## OMRFlag & 0.02 & 0.00 & 0.15 & 0.00 & 1.00 & 73926 \\
## RET\_m1 & 0.01 & 0.01 & 0.12 & -0.85 & 16.25 & 73906 \\
## TradeVol & 55.78 & 23.65 & 111.94 & 0.00 & 3881.63 & 73926 \\
## TradeVol\_scaled & 0.20 & 0.16 & 0.23 & -0.23 & 18.10 & 73851 \\
## Volatility & 0.02 & 0.02 & 0.01 & 0.00 & 0.79 & 73921 \\
## ShaRepYes & 0.52 & 1.00 & 0.50 & 0.00 & 1.00 & 73926 \\
## \hline
## \end{tabular}
## \end{table}
## Table 3. OLS -----
pdShaRep <- panel(ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
# mutate(reporting_ym = as.yearmon(reporting),
# filing_ym = as.yearmon(filing)) %>%
filter(RepIntensity <= 0.10) %>%
mutate(TradeVol_shaout = TradeVol / ShaOut) %>%
group_by(YM) %>%
mutate(BM_q1 = quantile(BM, na.rm = T)[2],
BM_q2 = quantile(BM, na.rm = T)[3],
BM_q3 = quantile(BM, na.rm = T)[4],
BM_q4 = quantile(BM, na.rm = T)[5],
RET_md = median(RET_m1, na.rm = T),
RET_mean = mean(RET_m1, na.rm = T),
frequency = mean(ShaRepYes, na.rm = T),
Volatility_md = median(Volatility, na.rm = T)
) %>%
ungroup() %>%
mutate(UnderPriced = BM > BM_q2,
BM_middle = (BM >= BM_q1) & (BM <= BM_q3) ,
PriceQ1 = 1*(BM <= BM_q1),
PriceQ2 = 2*(BM > BM_q1 & BM <= BM_q2),
PriceQ3 = 3*(BM > BM_q2 & BM <= BM_q3),
PriceQ4 = 4*(BM > BM_q3 & BM <= BM_q4),
PriceQ = PriceQ1 + PriceQ2 + PriceQ3 + PriceQ4,
HighVolatility = Volatility > Volatility_md,
RET_m1_0 = RET_m1 > 0,
) %>%
mutate(YQ = yearqtr(YM))# %>%
#mutate(group1 = (wv_bhar_reb1 > 0) + (wv_bhar_reb0 > 0),
# group3 = (wv_bhar_reb3 > 0) + (wv_bhar_reb0 > 0),
# group6 = (wv_bhar_reb6 > 0) + (wv_bhar_reb0 > 0)
# )
, ~ Ticker + time)
names(pdShaRep)
## [1] "YM" "AIAC" "ANews" "NewsAverage"
## [5] "NewsCount" "MarketAIA" "MarketAIAC" "CUSIP"
## [9] "CIK" "gvkey" "Name" "cusip8"
## [13] "CUSIP_tts" "AIA" "ShaRep3" "missing3"
## [17] "Price2" "Amihud_monthly" "OMRFlag" "Ticker"
## [21] "RET_m1" "Volatility" "AvePrice" "TradeVol"
## [25] "TradeVolDollar" "MarCap" "ShaOut" "ShaOutPrevious"
## [29] "datadate" "tic" "cusip" "atq"
## [33] "ceqq" "cheq" "oibdpq" "pstkq"
## [37] "seqq" "txditcq" "cdvcy" "costat"
## [41] "dlcq" "dlttq" "BE" "TD"
## [45] "CtA" "TA" "Year" "Quarter"
## [49] "Month" "RepIntensity" "RepIntensity_vol" "ShaRepYes"
## [53] "BM" "DivtoAsset" "EBITDA" "Leverage"
## [57] "TradeVol_scaled" "EBITDAtA" "time" "sic"
## [61] "shortintadj" "OMR_YM" "ProgramMonth" "ShortInterest"
## [65] "wv_bhar_reb3" "wv_bhar_nreb3" "wv_bhar_reb6" "wv_bhar_nreb6"
## [69] "wv_bhar_reb1" "wv_bhar_nreb1" "wv_bhar_reb0" "wv_bhar_nreb0"
## [73] "TradeVol_shaout" "BM_q1" "BM_q2" "BM_q3"
## [77] "BM_q4" "RET_md" "RET_mean" "frequency"
## [81] "Volatility_md" "UnderPriced" "BM_middle" "PriceQ1"
## [85] "PriceQ2" "PriceQ3" "PriceQ4" "PriceQ"
## [89] "HighVolatility" "RET_m1_0" "YQ"
dim(pdShaRep)
## [1] 73785 91
pdShaRep %>%
filter(!is.na(AIA) & !is.na(RepIntensity) & !is.na(RET_m1) ) %>%
dim
## [1] 73781 91
#### 3.1 RepIntensity -----
#### with AIA # updated September 22, 2023
RepInt_ols1 <- feols(fml = RepIntensity ~ AIA + l(RepIntensity, 1) +
log(Amihud_monthly) + OMRFlag + l(RET_m1, 1:3)
| Ticker + time , # + Ticker^yearqtr(YM),
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
## NOTE: 32,055 observations removed because of NA values (RHS: 32,055).
summary(RepInt_ols1)
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 41,730
## Fixed-effects: Ticker: 1,268, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.000575 0.000099 -5.79166 8.7811e-09 ***
## l(RepIntensity, 1) 0.178146 0.022932 7.76855 1.6306e-14 ***
## log(Amihud_monthly) -0.000913 0.000096 -9.50489 < 2.2e-16 ***
## OMRFlag 0.001625 0.000281 5.78256 9.2567e-09 ***
## l(RET_m1, 1) -0.003692 0.000451 -8.18406 6.6215e-16 ***
## l(RET_m1, 2) -0.002160 0.000387 -5.58595 2.8418e-08 ***
## l(RET_m1, 3) -0.001292 0.000367 -3.52064 4.4578e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005517 Adj. R2: 0.220536
## Within R2: 0.044414
feols(fml = AIA ~ MarketAIA, data = pdShaRep, cluster = "Ticker")
## OLS estimation, Dep. Var.: AIA
## Observations: 73,785
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.733782 0.01797333 40.8261 < 2.2e-16 ***
## MarketAIA -0.000064 0.00000381 -16.8216 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.729388 Adj. R2: 0.004128
feols(fml = AIA ~ MarketAIA| Ticker + time, data = pdShaRep, cluster = "Ticker")
## OLS estimation, Dep. Var.: AIA
## Observations: 73,785
## Fixed-effects: Ticker: 1,574, time: 141
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA -0.019173 0.049698 -0.385796 0.6997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.42057 Adj. R2: 0.661028
## Within R2: 1.493e-5
## this shows that the huge first stage coefficient comes from adding the fixed effects. The standard deviations in the first stage is correct in every sense.
RepInt_ols2 <- feols(fml = RepIntensity ~ AIA + l(RepIntensity, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + (CtA) + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled)
| Ticker + time, # + Ticker^yearqtr(YM), # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 33,949 observations removed because of NA and infinite values (RHS: 33,949).
summary(RepInt_ols2)
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,836
## Fixed-effects: Ticker: 1,237, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.000606 0.000100 -6.068851 1.7107e-09 ***
## l(RepIntensity, 1) 0.176948 0.024295 7.283258 5.7862e-13 ***
## log(Amihud_monthly) -0.002067 0.000148 -13.936154 < 2.2e-16 ***
## OMRFlag 0.001634 0.000293 5.566323 3.1879e-08 ***
## l(RET_m1, 1) -0.004014 0.000475 -8.455838 < 2.2e-16 ***
## l(RET_m1, 2) -0.001539 0.000391 -3.933056 8.8534e-05 ***
## l(RET_m1, 3) -0.000621 0.000371 -1.675381 9.4112e-02 .
## log(TA) -0.000874 0.000416 -2.100009 3.5930e-02 *
## CtA 0.002064 0.000799 2.581637 9.9476e-03 **
## EBITDAtA 0.004833 0.002626 1.840592 6.5921e-02 .
## Leverage 0.000229 0.001452 0.157515 8.7486e-01
## log(BM) 0.000928 0.000140 6.640155 4.6815e-11 ***
## DivtoAsset -0.007557 0.017767 -0.425352 6.7065e-01
## l(log(MarCap), 1) -0.001203 0.000383 -3.137677 1.7432e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230906
## Within R2: 0.056297
RepInt_ols3 <- feols(fml = RepIntensity ~ AIA + l(RepIntensity, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) + # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) + # l(log(TradeVol), 0:1)
ANews + NewsAverage + log(NewsCount) + ShortInterest + d(ShortInterest,1)
| Ticker + time , #+ Ticker^YQ , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = "Ticker")
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679).
summary(RepInt_ols3)
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,106
## Fixed-effects: Ticker: 1,216, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.000524 0.000100 -5.237942 1.9124e-07 ***
## l(RepIntensity, 1) 0.173905 0.024639 7.058087 2.8310e-12 ***
## log(Amihud_monthly) -0.002080 0.000153 -13.567421 < 2.2e-16 ***
## OMRFlag 0.001622 0.000302 5.376286 9.1109e-08 ***
## l(RET_m1, 1) -0.003798 0.000482 -7.875427 7.4844e-15 ***
## l(RET_m1, 2) -0.001370 0.000409 -3.348097 8.3861e-04 ***
## l(RET_m1, 3) -0.000525 0.000374 -1.403379 1.6076e-01
## log(TA) -0.000838 0.000440 -1.906006 5.6885e-02 .
## CtA 0.001891 0.000766 2.467321 1.3750e-02 *
## EBITDAtA 0.005294 0.002907 1.820923 6.8864e-02 .
## Leverage 0.000075 0.001499 0.050180 9.5999e-01
## log(BM) 0.000928 0.000139 6.667482 3.9400e-11 ***
## DivtoAsset -0.007896 0.017840 -0.442632 6.5811e-01
## l(log(MarCap), 1) -0.001254 0.000393 -3.187190 1.4732e-03 **
## ANews -0.000525 0.000155 -3.387819 7.2707e-04 ***
## NewsAverage 0.000170 0.000341 0.499205 6.1773e-01
## log(NewsCount) 0.000086 0.000103 0.843047 3.9937e-01
## ShortInterest -0.000685 0.001077 -0.636060 5.2486e-01
## d(ShortInterest, 1) 0.017957 0.006101 2.943102 3.3111e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005412 Adj. R2: 0.232359
## Within R2: 0.059071
#### with AIAC
{# now with AIAC
RepInt_ols1a <- feols(fml = RepIntensity ~ AIAC + l(RepIntensity, 1) +
log(Amihud_monthly) + OMRFlag + l(RET_m1, 1:3)
| Ticker + time , # + Ticker^yearqtr(YM),
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
RepInt_ols1a
RepInt_ols2a <- feols(fml = RepIntensity ~ AIAC + l(RepIntensity, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol) # +
# ANews + NewsAverage + NewsCount
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
RepInt_ols2a
RepInt_ols3a <- feols(fml = RepIntensity ~ AIAC + l(RepIntensity, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) + # d(Volatility,1) + l(Volatility, 1) + log(TradeVol) +
ANews + NewsAverage + log(NewsCount)
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
summary(RepInt_ols3a)
}
## NOTE: 32,055 observations removed because of NA values (RHS: 32,055).
## Warning in log(BM): NaNs produced
## NOTE: 33,949 observations removed because of NA and infinite values (RHS: 33,949).
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIAC -0.000747 0.000152 -4.925138 9.5799e-07 ***
## l(RepIntensity, 1) 0.175750 0.024374 7.210686 9.7058e-13 ***
## log(Amihud_monthly) -0.002079 0.000150 -13.864127 < 2.2e-16 ***
## OMRFlag 0.001647 0.000297 5.544518 3.6055e-08 ***
## l(RET_m1, 1) -0.003997 0.000478 -8.359415 < 2.2e-16 ***
## l(RET_m1, 2) -0.001574 0.000397 -3.967613 7.6797e-05 ***
## l(RET_m1, 3) -0.000585 0.000373 -1.567984 1.1714e-01
## log(TA) -0.000847 0.000421 -2.010562 4.4590e-02 *
## CtA 0.001766 0.000763 2.312816 2.0897e-02 *
## EBITDAtA 0.005015 0.002839 1.766529 7.7555e-02 .
## Leverage 0.000159 0.001460 0.108954 9.1326e-01
## log(BM) 0.000946 0.000139 6.823902 1.3882e-11 ***
## DivtoAsset -0.009577 0.017958 -0.533296 5.9393e-01
## l(log(MarCap), 1) -0.001173 0.000386 -3.036421 2.4445e-03 **
## ANews -0.000525 0.000155 -3.377167 7.5529e-04 ***
## NewsAverage 0.000086 0.000347 0.246650 8.0522e-01
## log(NewsCount) 0.000111 0.000103 1.081014 2.7990e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230793
## Within R2: 0.056916
etable(RepInt_ols1, RepInt_ols2, RepInt_ols3,
RepInt_ols1a, RepInt_ols2a, RepInt_ols3a,
# vcov = "Ticker",
# headers = paste("(", 1:3, ")", sep = ""),
tex = T)
## \begingroup
## \centering
## \begin{tabular}{lcccccc}
## \tabularnewline \midrule \midrule
## Dependent Variable: & \multicolumn{6}{c}{RepIntensity}\\
## Model: & (1) & (2) & (3) & (4) & (5) & (6)\\
## \midrule
## \emph{Variables}\\
## AIA & -0.0006$^{***}$ & -0.0006$^{***}$ & -0.0005$^{***}$ & & & \\
## & ($9.93\times 10^{-5}$) & ($9.98\times 10^{-5}$) & (0.0001) & & & \\
## l(RepIntensity,1) & 0.1781$^{***}$ & 0.1769$^{***}$ & 0.1739$^{***}$ & 0.1781$^{***}$ & 0.1769$^{***}$ & 0.1758$^{***}$\\
## & (0.0229) & (0.0243) & (0.0246) & (0.0229) & (0.0243) & (0.0244)\\
## log(Amihud\_monthly) & -0.0009$^{***}$ & -0.0021$^{***}$ & -0.0021$^{***}$ & -0.0009$^{***}$ & -0.0021$^{***}$ & -0.0021$^{***}$\\
## & ($9.6\times 10^{-5}$) & (0.0001) & (0.0002) & ($9.67\times 10^{-5}$) & (0.0001) & (0.0001)\\
## OMRFlag & 0.0016$^{***}$ & 0.0016$^{***}$ & 0.0016$^{***}$ & 0.0016$^{***}$ & 0.0016$^{***}$ & 0.0016$^{***}$\\
## & (0.0003) & (0.0003) & (0.0003) & (0.0003) & (0.0003) & (0.0003)\\
## l(RET\_m1,1) & -0.0037$^{***}$ & -0.0040$^{***}$ & -0.0038$^{***}$ & -0.0037$^{***}$ & -0.0040$^{***}$ & -0.0040$^{***}$\\
## & (0.0005) & (0.0005) & (0.0005) & (0.0005) & (0.0005) & (0.0005)\\
## l(RET\_m1,2) & -0.0022$^{***}$ & -0.0015$^{***}$ & -0.0014$^{***}$ & -0.0022$^{***}$ & -0.0016$^{***}$ & -0.0016$^{***}$\\
## & (0.0004) & (0.0004) & (0.0004) & (0.0004) & (0.0004) & (0.0004)\\
## l(RET\_m1,3) & -0.0013$^{***}$ & -0.0006$^{*}$ & -0.0005 & -0.0013$^{***}$ & -0.0006$^{*}$ & -0.0006\\
## & (0.0004) & (0.0004) & (0.0004) & (0.0004) & (0.0004) & (0.0004)\\
## log(TA) & & -0.0009$^{**}$ & -0.0008$^{*}$ & & -0.0009$^{**}$ & -0.0008$^{**}$\\
## & & (0.0004) & (0.0004) & & (0.0004) & (0.0004)\\
## CtA & & 0.0021$^{***}$ & 0.0019$^{**}$ & & 0.0021$^{***}$ & 0.0018$^{**}$\\
## & & (0.0008) & (0.0008) & & (0.0008) & (0.0008)\\
## EBITDAtA & & 0.0048$^{*}$ & 0.0053$^{*}$ & & 0.0048$^{*}$ & 0.0050$^{*}$\\
## & & (0.0026) & (0.0029) & & (0.0026) & (0.0028)\\
## Leverage & & 0.0002 & $7.52\times 10^{-5}$ & & 0.0003 & 0.0002\\
## & & (0.0015) & (0.0015) & & (0.0015) & (0.0015)\\
## log(BM) & & 0.0009$^{***}$ & 0.0009$^{***}$ & & 0.0009$^{***}$ & 0.0009$^{***}$\\
## & & (0.0001) & (0.0001) & & (0.0001) & (0.0001)\\
## DivtoAsset & & -0.0076 & -0.0079 & & -0.0080 & -0.0096\\
## & & (0.0178) & (0.0178) & & (0.0179) & (0.0180)\\
## l(log(MarCap),1) & & -0.0012$^{***}$ & -0.0013$^{***}$ & & -0.0012$^{***}$ & -0.0012$^{***}$\\
## & & (0.0004) & (0.0004) & & (0.0004) & (0.0004)\\
## ANews & & & -0.0005$^{***}$ & & & -0.0005$^{***}$\\
## & & & (0.0002) & & & (0.0002)\\
## NewsAverage & & & 0.0002 & & & $8.55\times 10^{-5}$\\
## & & & (0.0003) & & & (0.0003)\\
## log(NewsCount) & & & $8.64\times 10^{-5}$ & & & 0.0001\\
## & & & (0.0001) & & & (0.0001)\\
## ShortInterest & & & -0.0007 & & & \\
## & & & (0.0011) & & & \\
## d(ShortInterest,1) & & & 0.0180$^{***}$ & & & \\
## & & & (0.0061) & & & \\
## AIAC & & & & -0.0008$^{***}$ & -0.0009$^{***}$ & -0.0007$^{***}$\\
## & & & & (0.0001) & (0.0001) & (0.0002)\\
## \midrule
## \emph{Fixed-effects}\\
## Ticker & Yes & Yes & Yes & Yes & Yes & Yes\\
## time & Yes & Yes & Yes & Yes & Yes & Yes\\
## \midrule
## \emph{Fit statistics}\\
## Observations & 41,730 & 39,836 & 39,106 & 41,730 & 39,836 & 39,470\\
## R$^2$ & 0.24622 & 0.25699 & 0.25857 & 0.24623 & 0.25702 & 0.25703\\
## Within R$^2$ & 0.04441 & 0.05630 & 0.05907 & 0.04442 & 0.05634 & 0.05692\\
## \midrule \midrule
## \multicolumn{7}{l}{\emph{Clustered (Ticker) standard-errors in parentheses}}\\
## \multicolumn{7}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
## \end{tabular}
## \par\endgroup
#### 3.2 RepIntensity_vol -----
RepIntVol_ols1 <- feols(fml = RepIntensity_vol ~ AIA + l(RepIntensity_vol, 1) +
log(Amihud_monthly) + OMRFlag + l(RET_m1, 1:3)
| Ticker + time , # + Ticker^yearqtr(YM),
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
## NOTE: 32,055 observations removed because of NA values (RHS: 32,055).
RepIntVol_ols1
## OLS estimation, Dep. Var.: RepIntensity_vol
## Observations: 41,730
## Fixed-effects: Ticker: 1,268, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.005227 0.000531 -9.85006 < 2.2e-16 ***
## l(RepIntensity_vol, 1) 0.204657 0.035086 5.83305 6.9012e-09 ***
## log(Amihud_monthly) -0.001991 0.000461 -4.32217 1.6660e-05 ***
## OMRFlag 0.007098 0.001532 4.63412 3.9545e-06 ***
## l(RET_m1, 1) -0.010433 0.001772 -5.88753 5.0144e-09 ***
## l(RET_m1, 2) -0.005111 0.001617 -3.16167 1.6059e-03 **
## l(RET_m1, 3) -0.001731 0.001546 -1.11978 2.6302e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.029142 Adj. R2: 0.259332
## Within R2: 0.049006
RepIntVol_ols2 <- feols(fml = RepIntensity_vol ~ AIA + l(RepIntensity_vol, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol) # +
# ANews + NewsAverage + NewsCount
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 33,949 observations removed because of NA and infinite values (RHS: 33,949).
RepIntVol_ols2
## OLS estimation, Dep. Var.: RepIntensity_vol
## Observations: 39,836
## Fixed-effects: Ticker: 1,237, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.005290 0.000540 -9.797665 < 2.2e-16 ***
## l(RepIntensity_vol, 1) 0.204719 0.037161 5.508931 4.3887e-08 ***
## log(Amihud_monthly) -0.003926 0.000741 -5.297871 1.3860e-07 ***
## OMRFlag 0.007063 0.001603 4.406557 1.1413e-05 ***
## l(RET_m1, 1) -0.012041 0.001936 -6.220718 6.7606e-10 ***
## l(RET_m1, 2) -0.003203 0.001636 -1.957818 5.0476e-02 .
## l(RET_m1, 3) 0.000216 0.001619 0.133216 8.9404e-01
## log(TA) 0.000591 0.001973 0.299439 7.6466e-01
## CtA 0.014256 0.004116 3.463897 5.5063e-04 ***
## EBITDAtA 0.010498 0.011020 0.952598 3.4098e-01
## Leverage -0.018310 0.007050 -2.597105 9.5129e-03 **
## log(BM) 0.003289 0.000680 4.837162 1.4829e-06 ***
## DivtoAsset -0.069392 0.103978 -0.667376 5.0466e-01
## l(log(MarCap), 1) -0.004146 0.001840 -2.253799 2.4383e-02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.028965 Adj. R2: 0.264714
## Within R2: 0.053432
RepIntVol_ols3 <- feols(fml = RepIntensity_vol ~ AIA + l(RepIntensity_vol, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) + # d(Volatility,1) + l(Volatility, 1) + log(TradeVol) +
ANews + NewsAverage + log(NewsCount)
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
summary(RepIntVol_ols3)
## OLS estimation, Dep. Var.: RepIntensity_vol
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.003871 0.000538 -7.189933 1.1232e-12 ***
## l(RepIntensity_vol, 1) 0.202562 0.037373 5.420030 7.1670e-08 ***
## log(Amihud_monthly) -0.004548 0.000740 -6.144700 1.0808e-09 ***
## OMRFlag 0.007716 0.001614 4.780240 1.9624e-06 ***
## l(RET_m1, 1) -0.012096 0.001967 -6.148069 1.0587e-09 ***
## l(RET_m1, 2) -0.003259 0.001656 -1.967342 4.9369e-02 *
## l(RET_m1, 3) 0.000400 0.001634 0.244701 8.0673e-01
## log(TA) 0.000777 0.002002 0.388053 6.9804e-01
## CtA 0.013133 0.003997 3.285332 1.0474e-03 **
## EBITDAtA 0.009382 0.011922 0.786902 4.3149e-01
## Leverage -0.018156 0.007097 -2.558361 1.0636e-02 *
## log(BM) 0.003343 0.000681 4.908441 1.0415e-06 ***
## DivtoAsset -0.069555 0.108842 -0.639045 5.2291e-01
## l(log(MarCap), 1) -0.004245 0.001867 -2.273896 2.3144e-02 *
## ANews -0.001105 0.000796 -1.388062 1.6537e-01
## NewsAverage -0.002372 0.001582 -1.499861 1.3391e-01
## log(NewsCount) -0.002199 0.000563 -3.904652 9.9494e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.028979 Adj. R2: 0.266223
## Within R2: 0.055695
#### with AIAC
{# now with AIAC
RepIntVol_ols1a <- feols(fml = RepIntensity_vol ~ AIAC + l(RepIntensity_vol, 1) +
log(Amihud_monthly) + OMRFlag + l(RET_m1, 1:3)
| Ticker + time , # + Ticker^yearqtr(YM),
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
RepIntVol_ols1a
RepIntVol_ols2a <- feols(fml = RepIntensity_vol ~ AIAC + l(RepIntensity_vol, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol) # +
# ANews + NewsAverage + NewsCount
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
RepIntVol_ols2a
RepIntVol_ols3a <- feols(fml = RepIntensity_vol ~ AIAC + l(RepIntensity_vol, 1) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) + # d(Volatility,1) + l(Volatility, 1) + log(TradeVol) +
ANews + NewsAverage + log(NewsCount)
| Ticker + time, # + Ticker^yearqtr(YM), # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
summary(RepIntVol_ols3a)
}
## NOTE: 32,055 observations removed because of NA values (RHS: 32,055).
## Warning in log(BM): NaNs produced
## NOTE: 33,949 observations removed because of NA and infinite values (RHS: 33,949).
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
## OLS estimation, Dep. Var.: RepIntensity_vol
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIAC -0.005692 0.000792 -7.189856 1.1238e-12 ***
## l(RepIntensity_vol, 1) 0.202327 0.037366 5.414815 7.3740e-08 ***
## log(Amihud_monthly) -0.004633 0.000739 -6.267579 5.0661e-10 ***
## OMRFlag 0.007700 0.001615 4.766996 2.0934e-06 ***
## l(RET_m1, 1) -0.012112 0.001969 -6.151820 1.0347e-09 ***
## l(RET_m1, 2) -0.003346 0.001657 -2.019091 4.3695e-02 *
## l(RET_m1, 3) 0.000287 0.001635 0.175487 8.6073e-01
## log(TA) 0.000798 0.001999 0.399313 6.8973e-01
## CtA 0.013151 0.004001 3.287170 1.0406e-03 **
## EBITDAtA 0.009350 0.011996 0.779438 4.3587e-01
## Leverage -0.017764 0.007092 -2.504864 1.2379e-02 *
## log(BM) 0.003359 0.000680 4.941161 8.8392e-07 ***
## DivtoAsset -0.072117 0.109183 -0.660515 5.0905e-01
## l(log(MarCap), 1) -0.004165 0.001865 -2.233140 2.5719e-02 *
## ANews -0.001071 0.000798 -1.341361 1.8005e-01
## NewsAverage -0.002526 0.001591 -1.587685 1.1261e-01
## log(NewsCount) -0.002147 0.000564 -3.803775 1.4952e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.028976 Adj. R2: 0.266362
## Within R2: 0.055875
etable(RepIntVol_ols1, RepIntVol_ols2, RepIntVol_ols3,
RepIntVol_ols1a, RepIntVol_ols2a, RepIntVol_ols3a,
# vcov = "Ticker",
# headers = paste("(", 1:3, ")", sep = ""),
tex = T)
## \begingroup
## \centering
## \begin{tabular}{lcccccc}
## \tabularnewline \midrule \midrule
## Dependent Variable: & \multicolumn{6}{c}{RepIntensity\_vol}\\
## Model: & (1) & (2) & (3) & (4) & (5) & (6)\\
## \midrule
## \emph{Variables}\\
## AIA & -0.0052$^{***}$ & -0.0053$^{***}$ & -0.0039$^{***}$ & & & \\
## & (0.0005) & (0.0005) & (0.0005) & & & \\
## l(RepIntensity\_vol,1) & 0.2047$^{***}$ & 0.2047$^{***}$ & 0.2026$^{***}$ & 0.2043$^{***}$ & 0.2044$^{***}$ & 0.2023$^{***}$\\
## & (0.0351) & (0.0372) & (0.0374) & (0.0351) & (0.0372) & (0.0374)\\
## log(Amihud\_monthly) & -0.0020$^{***}$ & -0.0039$^{***}$ & -0.0045$^{***}$ & -0.0021$^{***}$ & -0.0040$^{***}$ & -0.0046$^{***}$\\
## & (0.0005) & (0.0007) & (0.0007) & (0.0005) & (0.0007) & (0.0007)\\
## OMRFlag & 0.0071$^{***}$ & 0.0071$^{***}$ & 0.0077$^{***}$ & 0.0071$^{***}$ & 0.0070$^{***}$ & 0.0077$^{***}$\\
## & (0.0015) & (0.0016) & (0.0016) & (0.0015) & (0.0016) & (0.0016)\\
## l(RET\_m1,1) & -0.0104$^{***}$ & -0.0120$^{***}$ & -0.0121$^{***}$ & -0.0105$^{***}$ & -0.0120$^{***}$ & -0.0121$^{***}$\\
## & (0.0018) & (0.0019) & (0.0020) & (0.0018) & (0.0019) & (0.0020)\\
## l(RET\_m1,2) & -0.0051$^{***}$ & -0.0032$^{*}$ & -0.0033$^{**}$ & -0.0053$^{***}$ & -0.0033$^{**}$ & -0.0033$^{**}$\\
## & (0.0016) & (0.0016) & (0.0017) & (0.0016) & (0.0016) & (0.0017)\\
## l(RET\_m1,3) & -0.0017 & 0.0002 & 0.0004 & -0.0019 & $6.41\times 10^{-5}$ & 0.0003\\
## & (0.0015) & (0.0016) & (0.0016) & (0.0015) & (0.0016) & (0.0016)\\
## log(TA) & & 0.0006 & 0.0008 & & 0.0006 & 0.0008\\
## & & (0.0020) & (0.0020) & & (0.0020) & (0.0020)\\
## CtA & & 0.0143$^{***}$ & 0.0131$^{***}$ & & 0.0143$^{***}$ & 0.0132$^{***}$\\
## & & (0.0041) & (0.0040) & & (0.0041) & (0.0040)\\
## EBITDAtA & & 0.0105 & 0.0094 & & 0.0105 & 0.0094\\
## & & (0.0110) & (0.0119) & & (0.0111) & (0.0120)\\
## Leverage & & -0.0183$^{***}$ & -0.0182$^{**}$ & & -0.0178$^{**}$ & -0.0178$^{**}$\\
## & & (0.0070) & (0.0071) & & (0.0070) & (0.0071)\\
## log(BM) & & 0.0033$^{***}$ & 0.0033$^{***}$ & & 0.0033$^{***}$ & 0.0034$^{***}$\\
## & & (0.0007) & (0.0007) & & (0.0007) & (0.0007)\\
## DivtoAsset & & -0.0694 & -0.0696 & & -0.0731 & -0.0721\\
## & & (0.1040) & (0.1088) & & (0.1049) & (0.1092)\\
## l(log(MarCap),1) & & -0.0041$^{**}$ & -0.0042$^{**}$ & & -0.0040$^{**}$ & -0.0042$^{**}$\\
## & & (0.0018) & (0.0019) & & (0.0018) & (0.0019)\\
## ANews & & & -0.0011 & & & -0.0011\\
## & & & (0.0008) & & & (0.0008)\\
## NewsAverage & & & -0.0024 & & & -0.0025\\
## & & & (0.0016) & & & (0.0016)\\
## log(NewsCount) & & & -0.0022$^{***}$ & & & -0.0021$^{***}$\\
## & & & (0.0006) & & & (0.0006)\\
## AIAC & & & & -0.0076$^{***}$ & -0.0077$^{***}$ & -0.0057$^{***}$\\
## & & & & (0.0008) & (0.0008) & (0.0008)\\
## \midrule
## \emph{Fixed-effects}\\
## Ticker & Yes & Yes & Yes & Yes & Yes & Yes\\
## time & Yes & Yes & Yes & Yes & Yes & Yes\\
## \midrule
## \emph{Fit statistics}\\
## Observations & 41,730 & 39,836 & 39,470 & 41,730 & 39,836 & 39,470\\
## R$^2$ & 0.28374 & 0.28965 & 0.29125 & 0.28388 & 0.28981 & 0.29138\\
## Within R$^2$ & 0.04901 & 0.05343 & 0.05570 & 0.04919 & 0.05365 & 0.05588\\
## \midrule \midrule
## \multicolumn{7}{l}{\emph{Clustered (Ticker) standard-errors in parentheses}}\\
## \multicolumn{7}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
## \end{tabular}
## \par\endgroup
if (0 ==1) {
RepInt_ols_back <- feols(fml = RepIntensity ~ AIA + l(RepIntensity, -1) +
Amihud_monthly + OMRFlag + # log(1 + NewsCount) +
l(RET_m1, -1:0) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
log(MarCap) + d(Volatility,1) + l(Volatility, -1) + log(TradeVol) +
ANews + NewsAverage + NewsCount
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker"))
pdShaRep3 <- panel(ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
filter(RepIntensity <= 0.10) %>%
group_by(YM) %>%
mutate(BM_med = median(BM, na.rm = T),
Volatility_md = median(Volatility, na.rm = T)
) %>%
ungroup() %>%
mutate(UnderPriced = BM > BM_med,
HighVolatility = Volatility > Volatility_md
), ~ Ticker + time)
feols(fml = RepIntensity ~
AIA + HighVolatility + AIA:HighVolatility + l(RepIntensity, 1) +
# AIA + UnderPriced + AIA:UnderPriced + l(RepIntensity, 1) +
# AIA + AIA:log(BM) + l(RepIntensity, 1) +
Amihud_monthly + OMRFlag +
l(RET_m1, 1:3) + log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) + d(Volatility,1) + l(Volatility, 1) + log(TradeVol) +
ANews + NewsAverage + log(NewsCount)
| Ticker + time, # + Ticker^yearqtr(YM), # + Ticker^Year,
data = pdShaRep3, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker")) %>%
summary()
}
##### choose between good and bad firms
feols(fml = RepIntensity ~ AIA*log(BM) + l(RepIntensity, 1) +
ANews + NewsAverage + NewsCount +
Amihud_monthly + OMRFlag + # log(1 + NewsCount) +
l(RET_m1, 1:3) + log(TA) + CtA + (EBITDAtA) + log(Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) + d(Volatility,1) + l(Volatility, 1) + log(TradeVol)
| Ticker + time , # + Ticker^Year,
data = pdShaRep, # %>% filter(RepIntensity_vol < 0.6), # %>% filter(missing3 == F), # %>% filter(RepIntensity > 34308*10^-10),
cluster = c("Ticker")) %>%
summary()
## Warning in log(BM): NaNs produced
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.000952243 0.000139134 -6.844055 1.2124e-11 ***
## log(BM) 0.001207994 0.000148512 8.133968 1.0081e-15 ***
## l(RepIntensity, 1) 0.176546200 0.024521407 7.199677 1.0488e-12 ***
## ANews -0.000528233 0.000163446 -3.231854 1.2626e-03 **
## NewsAverage 0.000196618 0.000370157 0.531175 5.9539e-01
## NewsCount -0.000000191 0.000000356 -0.535340 5.9251e-01
## Amihud_monthly 0.011245068 0.009039153 1.244040 2.1372e-01
## OMRFlag 0.001620989 0.000296177 5.473038 5.3583e-08 ***
## l(RET_m1, 1) -0.002570903 0.000455517 -5.643919 2.0624e-08 ***
## l(RET_m1, 2) -0.001276676 0.000391708 -3.259259 1.1476e-03 **
## l(RET_m1, 3) -0.000551883 0.000367485 -1.501787 1.3341e-01
## log(TA) -0.002143216 0.000351934 -6.089817 1.5095e-09 ***
## CtA 0.002077677 0.000783683 2.651171 8.1245e-03 **
## EBITDAtA 0.007164967 0.003061652 2.340229 1.9431e-02 *
## log(Leverage) 0.001249335 0.000306828 4.071771 4.9643e-05 ***
## DivtoAsset -0.019308889 0.020258124 -0.953143 3.4071e-01
## l(log(MarCap), 1) 0.001603031 0.000313150 5.119048 3.5615e-07 ***
## d(Volatility, 1) -0.043143699 0.007357268 -5.864092 5.7985e-09 ***
## l(Volatility, 1) -0.041742714 0.008921180 -4.679058 3.2014e-06 ***
## log(TradeVol) 0.001964307 0.000168222 11.676885 < 2.2e-16 ***
## AIA:log(BM) -0.000304146 0.000073882 -4.116639 4.1011e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005404 Adj. R2: 0.231338
## Within R2: 0.057682
## 4. IV regression 2sls* ------
pdShaRep <- panel(ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
mutate(AIA = case_when(AIA < 0 ~ 0, AIA >= 0 ~ AIA), ## newly added for very negative
MarketAIA = case_when(MarketAIA < 0 ~ 0, MarketAIA >= 0 ~ MarketAIA)) %>%
mutate(lambda = TradeVol / ShaOut) %>%
# mutate(Amihud_monthly = log(Amihud_monthly)) %>%
# filter(OMRFlag == 0) %>%
filter(RepIntensity <= 0.10) , ~ Ticker + time)
pdShaRep[,c("AIA", "MarketAIA")] %>% describe()
## vars n mean sd median trimmed mad min max range skew
## AIA 1 73785 0.74 0.73 0.52 0.62 0.56 0 4.00 4.00 1.54
## MarketAIA 2 73785 0.64 0.16 0.64 0.64 0.15 0 0.97 0.97 -0.64
## kurtosis se
## AIA 2.69 0
## MarketAIA 1.75 0
feols(fml = (AIA) ~ (MarketAIA) | Ticker + time,
data = pdShaRep,
cluster = c("Ticker")
) ## if just regress the two, the coefficnet becomes insignificant. ## May 13, 2024
## OLS estimation, Dep. Var.: AIA
## Observations: 73,785
## Fixed-effects: Ticker: 1,574, time: 141
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA -1173.12 13.2799 -88.338 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.21524 Adj. R2: 0.911216
## Within R2: 0.738083
feols(fml = (AIA) ~ (MarketAIA) + l(RepIntensity, 1) | Ticker + time + Ticker^year(YM),
data = pdShaRep,
cluster = c("Ticker")
) ## it seems that past firm repurchase behavior significantly predicts AIA! ## May 13, 2024
## NOTE: 12,149 observations removed because of NA values (RHS: 12,149).
## OLS estimation, Dep. Var.: AIA
## Observations: 61,636
## Fixed-effects: Ticker: 1,526, time: 128, Ticker^year(YM): 9,212
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA -1185.257684 12.550526 -94.438884 < 2.2e-16 ***
## l(RepIntensity, 1) -0.077063 0.104607 -0.736689 0.46142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.163303 Adj. R2: 0.93587
## Within R2: 0.778092
feols(fml = log(AIA) ~ log(MarketAIA) + l(RepIntensity, 1) | Ticker + time + Ticker^year(YM),
data = pdShaRep,
cluster = c("Ticker")
) ## seems using log can significantly improve the estimate. But why? ## May 13, 2024
## NOTE: 19,398 observations removed because of NA and infinite values (LHS: 9,531, RHS: 12,149).
## OLS estimation, Dep. Var.: log(AIA)
## Observations: 54,387
## Fixed-effects: Ticker: 1,436, time: 128, Ticker^year(YM): 8,511
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## log(MarketAIA) -1037.403909 35.961489 -28.847635 < 2.2e-16 ***
## l(RepIntensity, 1) 0.036455 0.355602 0.102517 0.91836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.373104 Adj. R2: 0.81152
## Within R2: 0.584609
fe_2sls <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + # log(ProgramMonth) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ # + sic*time
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
summary(fe_2sls, stage = 1:2)
## IV: First stage: AIA
## TSLS estimation, Dep. Var.: AIA, Endo.: AIA, Instr.: MarketAIA
## First stage: Dep. Var.: AIA
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA -1246.246377 5.213920 -239.022901 < 2.2e-16 ***
## l(RepIntensity, 1) -0.038911 0.108752 -0.357791 7.2056e-01
## ANews 0.012178 0.004044 3.011646 2.6515e-03 **
## NewsAverage -0.021503 0.008629 -2.492072 1.2831e-02 *
## log(NewsCount) 0.017287 0.002222 7.780849 1.5196e-14 ***
## log(Amihud_monthly) -0.006228 0.003398 -1.832821 6.7071e-02 .
## OMRFlag -0.005216 0.003510 -1.486078 1.3752e-01
## l(RET_m1, 1) -0.014092 0.008494 -1.659027 9.7366e-02 .
## l(RET_m1, 2) -0.005770 0.008700 -0.663160 5.0735e-01
## l(RET_m1, 3) 0.002708 0.011858 0.228379 8.1939e-01
## log(TA) 0.002704 0.011175 0.241974 8.0884e-01
## CtA -0.020015 0.017653 -1.133812 2.5709e-01
## EBITDAtA 0.045051 0.040707 1.106719 2.6863e-01
## Leverage -0.028830 0.041254 -0.698833 4.8479e-01
## log(BM) -0.000962 0.002774 -0.347000 7.2865e-01
## DivtoAsset -0.856941 0.631948 -1.356031 1.7534e-01
## l(log(MarCap), 1) -0.005619 0.008903 -0.631094 5.2810e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.146318 Adj. R2: 0.954451
## Within R2: 0.866112
## F-test (1st stage): stat = 211,915.5, p < 2.2e-16, on 1 and 39,351 DoF.
##
## IV: Second stage
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000564 0.000110 -5.132138 3.3273e-07 ***
## l(RepIntensity, 1) 0.175804 0.024377 7.211989 9.6171e-13 ***
## ANews -0.000525 0.000155 -3.379464 7.4907e-04 ***
## NewsAverage 0.000131 0.000347 0.377680 7.0573e-01
## log(NewsCount) 0.000111 0.000102 1.087722 2.7693e-01
## log(Amihud_monthly) -0.002069 0.000150 -13.818555 < 2.2e-16 ***
## OMRFlag 0.001651 0.000297 5.562898 3.2538e-08 ***
## l(RET_m1, 1) -0.004001 0.000478 -8.369848 < 2.2e-16 ***
## l(RET_m1, 2) -0.001564 0.000396 -3.944144 8.4615e-05 ***
## l(RET_m1, 3) -0.000573 0.000373 -1.535225 1.2499e-01
## log(TA) -0.000848 0.000421 -2.015178 4.4104e-02 *
## CtA 0.001771 0.000764 2.319118 2.0552e-02 *
## EBITDAtA 0.005004 0.002832 1.766994 7.7478e-02 .
## Leverage 0.000123 0.001460 0.084518 9.3266e-01
## log(BM) 0.000945 0.000139 6.816235 1.4614e-11 ***
## DivtoAsset -0.009282 0.017855 -0.519857 6.0326e-01
## l(log(MarCap), 1) -0.001178 0.000386 -3.049153 2.3440e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230749
## Within R2: 0.056862
## F-test (1st stage), AIA: stat = 211,915.5 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 2.08848, p = 0.148421, on 1 and 38,122 DoF.
## %% NOTE: the results is robustness when adding industry-year-month interactions and even firm-year-quarter interactions, although the magnitude is halved for the latter case and the significance level drops to 5% level. [May 4, 2024]
## other alternative controls:
feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ # + sic*time
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679).
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,106
## Fixed-effects: Ticker: 1,216, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000542 0.000109 -4.983317 7.1548e-07 ***
## l(RepIntensity, 1) 0.169328 0.024563 6.893594 8.7206e-12 ***
## ANews -0.000568 0.000155 -3.660698 2.6233e-04 ***
## NewsAverage 0.000241 0.000342 0.703250 4.8203e-01
## log(NewsCount) 0.000108 0.000102 1.060362 2.8919e-01
## log(Amihud_monthly) -0.002079 0.000152 -13.713430 < 2.2e-16 ***
## OMRFlag 0.000445 0.000337 1.321368 1.8663e-01
## l(RET_m1, 1) -0.003827 0.000483 -7.925154 5.1211e-15 ***
## l(RET_m1, 2) -0.001392 0.000408 -3.416038 6.5638e-04 ***
## l(RET_m1, 3) -0.000525 0.000372 -1.410464 1.5866e-01
## log(TA) -0.000833 0.000434 -1.920582 5.5018e-02 .
## CtA 0.002030 0.000774 2.621826 8.8553e-03 **
## EBITDAtA 0.004657 0.002830 1.645181 1.0019e-01
## Leverage 0.000337 0.001479 0.228011 8.1968e-01
## log(BM) 0.000932 0.000139 6.703427 3.1094e-11 ***
## DivtoAsset -0.010046 0.017029 -0.589949 5.5533e-01
## log(ProgramMonth) -0.000497 0.000064 -7.762568 1.7572e-14 ***
## l(log(MarCap), 1) -0.001285 0.000390 -3.290339 1.0294e-03 **
## ShortInterest -0.000960 0.001053 -0.911698 3.6211e-01
## d(ShortInterest, 1) 0.017937 0.006102 2.939345 3.3512e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005403 Adj. R2: 0.234962
## Within R2: 0.062287
## F-test (1st stage), AIA: stat = 208,231.9 , p < 2.2e-16, on 1 and 38,984 DoF.
## Wu-Hausman: stat = 2.12353, p = 0.14506, on 1 and 37,768 DoF.
fe_2sls_logIV <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + # log(ProgramMonth) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | log(AIA) ~ log(MarketAIA), # + Ticker^yearqtr(YM) # + Ticker^YQ # + sic*time
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 38,199 observations removed because of NA and infinite values (RHS: 34,315, IV: 9,531/468).
summary(fe_2sls_logIV, stage = 1:2) ## change to use log(AIA) ## May 13, 2024
## IV: First stage: log(AIA)
## TSLS estimation, Dep. Var.: log(AIA), Endo.: log(AIA), Instr.: log(MarketAIA)
## First stage: Dep. Var.: log(AIA)
## Observations: 35,586
## Fixed-effects: Ticker: 1,178, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## log(MarketAIA) -988.220996 34.530673 -28.618643 < 2.2e-16 ***
## l(RepIntensity, 1) 0.719321 0.418508 1.718777 8.5918e-02 .
## ANews 0.086669 0.019397 4.468238 8.6465e-06 ***
## NewsAverage -0.207212 0.071528 -2.896917 3.8382e-03 **
## log(NewsCount) 0.173158 0.011706 14.792797 < 2.2e-16 ***
## log(Amihud_monthly) 0.017308 0.009620 1.799138 7.2253e-02 .
## OMRFlag -0.000952 0.012384 -0.076909 9.3871e-01
## l(RET_m1, 1) 0.008257 0.028501 0.289729 7.7207e-01
## l(RET_m1, 2) -0.001819 0.027310 -0.066607 9.4691e-01
## l(RET_m1, 3) -0.023145 0.023941 -0.966783 3.3385e-01
## log(TA) 0.001530 0.027587 0.055473 9.5577e-01
## CtA -0.035170 0.058978 -0.596328 5.5107e-01
## EBITDAtA -0.381092 0.184228 -2.068590 3.8803e-02 *
## Leverage -0.027939 0.092993 -0.300442 7.6389e-01
## log(BM) 0.002513 0.009544 0.263347 7.9233e-01
## DivtoAsset -3.651904 3.710999 -0.984076 3.2528e-01
## l(log(MarCap), 1) 0.031667 0.024479 1.293644 1.9604e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.386219 Adj. R2: 0.823898
## Within R2: 0.632276
## F-test (1st stage): stat = 47,164.2, p < 2.2e-16, on 1 and 35,467 DoF.
##
## IV: Second stage
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: log(AIA), Instr.: log(MarketAIA)
## Second stage: Dep. Var.: RepIntensity
## Observations: 35,586
## Fixed-effects: Ticker: 1,178, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_log(AIA) -0.000410 0.000085 -4.803000 1.7643e-06 ***
## l(RepIntensity, 1) 0.185899 0.024992 7.438334 1.9596e-13 ***
## ANews -0.000405 0.000161 -2.516796 1.1975e-02 *
## NewsAverage -0.000024 0.000355 -0.068682 9.4525e-01
## log(NewsCount) 0.000133 0.000117 1.141208 2.5402e-01
## log(Amihud_monthly) -0.002111 0.000162 -13.036779 < 2.2e-16 ***
## OMRFlag 0.001594 0.000304 5.250918 1.7946e-07 ***
## l(RET_m1, 1) -0.003765 0.000495 -7.608960 5.6386e-14 ***
## l(RET_m1, 2) -0.001386 0.000416 -3.332431 8.8766e-04 ***
## l(RET_m1, 3) -0.000731 0.000378 -1.934065 5.3345e-02 .
## log(TA) -0.000923 0.000438 -2.107310 3.5302e-02 *
## CtA 0.001683 0.000764 2.203949 2.7721e-02 *
## EBITDAtA 0.004367 0.002923 1.494047 1.3543e-01
## Leverage 0.000391 0.001458 0.267946 7.8879e-01
## log(BM) 0.000920 0.000145 6.356548 2.9449e-10 ***
## DivtoAsset -0.019550 0.019027 -1.027502 3.0440e-01
## l(log(MarCap), 1) -0.001082 0.000403 -2.686497 7.3222e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005402 Adj. R2: 0.23032
## Within R2: 0.05919
## F-test (1st stage), log(AIA): stat = 47,164.2 , p < 2.2e-16 , on 1 and 35,467 DoF.
## Wu-Hausman: stat = 4.70637, p = 0.030058, on 1 and 34,289 DoF.
feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + # log(ProgramMonth) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | (AIA) ~ (MarketAIA), # + Ticker^yearqtr(YM) # + Ticker^YQ # + sic*time
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker", "time"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker & time)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000564 0.000123 -4.598672 1.2353e-05 ***
## l(RepIntensity, 1) 0.175804 0.023964 7.336253 5.6550e-11 ***
## ANews -0.000525 0.000155 -3.393476 9.8699e-04 ***
## NewsAverage 0.000131 0.000344 0.381274 7.0380e-01
## log(NewsCount) 0.000111 0.000104 1.071640 2.8644e-01
## log(Amihud_monthly) -0.002069 0.000174 -11.904834 < 2.2e-16 ***
## OMRFlag 0.001651 0.000300 5.511734 2.7306e-07 ***
## l(RET_m1, 1) -0.004001 0.000657 -6.092707 2.0338e-08 ***
## l(RET_m1, 2) -0.001564 0.000506 -3.093071 2.5618e-03 **
## l(RET_m1, 3) -0.000573 0.000415 -1.379981 1.7064e-01
## log(TA) -0.000848 0.000447 -1.895934 6.0827e-02 .
## CtA 0.001771 0.000690 2.567527 1.1708e-02 *
## EBITDAtA 0.005004 0.002723 1.837789 6.9033e-02 .
## Leverage 0.000123 0.001550 0.079576 9.3673e-01
## log(BM) 0.000945 0.000131 7.227267 9.6047e-11 ***
## DivtoAsset -0.009282 0.020288 -0.457521 6.4828e-01
## l(log(MarCap), 1) -0.001178 0.000379 -3.111589 2.4198e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230749
## Within R2: 0.056862
## F-test (1st stage), AIA: stat = 212,459.4 , p < 2.2e-16 , on 1 and 39,452 DoF.
## Wu-Hausman: stat = 2.08848, p = 0.148421, on 1 and 38,122 DoF.
feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time + sic*time | AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ #
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679, Fixed-effects: 1,243).
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,106
## Fixed-effects: Ticker: 1,216, time: 102, sic: 293, time:sic: 17,417
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000614 0.000150 -4.095284 4.4955e-05 ***
## l(RepIntensity, 1) 0.155455 0.035916 4.328342 1.6259e-05 ***
## ANews -0.000375 0.000237 -1.579275 1.1453e-01
## NewsAverage 0.000333 0.000504 0.661757 5.0825e-01
## log(NewsCount) 0.000089 0.000164 0.539580 5.8959e-01
## log(Amihud_monthly) -0.002309 0.000237 -9.738466 < 2.2e-16 ***
## OMRFlag 0.000238 0.000507 0.470075 6.3839e-01
## l(RET_m1, 1) -0.003848 0.000802 -4.795919 1.8198e-06 ***
## l(RET_m1, 2) -0.001418 0.000668 -2.122507 3.3997e-02 *
## l(RET_m1, 3) 0.000063 0.000692 0.091122 9.2741e-01
## log(TA) 0.000122 0.000723 0.169169 8.6569e-01
## CtA 0.001969 0.001103 1.784903 7.4526e-02 .
## EBITDAtA 0.000288 0.003410 0.084314 9.3282e-01
## Leverage -0.002031 0.002288 -0.887708 3.7487e-01
## log(BM) 0.000388 0.000265 1.466472 1.4278e-01
## DivtoAsset -0.085269 0.113446 -0.751620 4.5243e-01
## log(ProgramMonth) -0.000434 0.000100 -4.351621 1.4648e-05 ***
## l(log(MarCap), 1) -0.002703 0.000656 -4.120572 4.0355e-05 ***
## ShortInterest -0.002359 0.001707 -1.381835 1.6728e-01
## d(ShortInterest, 1) 0.023761 0.007153 3.321878 9.2073e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.00398 Adj. R2: 0.218354
## Within R2: 0.052638
## F-test (1st stage), AIA: stat = 81,168.0 , p < 2.2e-16 , on 1 and 21,276 DoF.
## Wu-Hausman: stat = 2.21596, p = 0.136605, on 1 and 20,060 DoF.
feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time + Ticker^yearqtr(YM) | AIA ~ MarketAIA, # # + Ticker^YQ #
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679).
## The exogenous variable 'DivtoAsset' have been removed because of collinearity (see $collin.var).
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,106
## Fixed-effects: Ticker: 1,216, time: 102, Ticker^yearqtr(YM): 14,330
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000360 0.000127 -2.837651 4.6202e-03 **
## l(RepIntensity, 1) -0.241473 0.016391 -14.731862 < 2.2e-16 ***
## ANews -0.000419 0.000174 -2.406750 1.6244e-02 *
## NewsAverage -0.000206 0.000389 -0.530672 5.9574e-01
## log(NewsCount) 0.000037 0.000121 0.308599 7.5768e-01
## log(Amihud_monthly) -0.002349 0.000222 -10.568056 < 2.2e-16 ***
## OMRFlag -0.001194 0.000526 -2.267388 2.3542e-02 *
## l(RET_m1, 1) -0.002655 0.000640 -4.147908 3.5886e-05 ***
## l(RET_m1, 2) -0.002952 0.000790 -3.736964 1.9491e-04 ***
## l(RET_m1, 3) -0.001628 0.000594 -2.738064 6.2705e-03 **
## log(TA) 0.001418 0.002678 0.529464 5.9658e-01
## CtA 0.016514 0.003690 4.475100 8.3540e-06 ***
## EBITDAtA -0.021848 0.005800 -3.766794 1.7328e-04 ***
## Leverage -0.010991 0.007630 -1.440608 1.4995e-01
## log(BM) 0.007076 0.001360 5.203745 2.2909e-07 ***
## log(ProgramMonth) -0.000731 0.000204 -3.584367 3.5129e-04 ***
## l(log(MarCap), 1) 0.001087 0.000997 1.090759 2.7560e-01
## ShortInterest 0.000884 0.007167 0.123275 9.0191e-01
## d(ShortInterest, 1) 0.010209 0.008675 1.176770 2.3952e-01
## ... 1 variable was removed because of collinearity (DivtoAsset)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.003972 Adj. R2: 0.333894
## Within R2: 0.077322
## F-test (1st stage), AIA: stat = 200,845.5, p < 2.2e-16, on 1 and 38,985 DoF.
## Wu-Hausman: stat = NA , p = NA , on 1 and 23,440 DoF.
feols(fml = RepIntensity ~ AIA + MarketAIA + l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol) # + lambda
| Ticker + time , # Ticker^yearqtr(YM)
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.000267 0.000142 -1.871037 6.1578e-02 .
## MarketAIA 0.371119 0.202356 1.833989 6.6897e-02 .
## l(RepIntensity, 1) 0.175816 0.024375 7.213094 9.5426e-13 ***
## ANews -0.000529 0.000155 -3.400685 6.9377e-04 ***
## NewsAverage 0.000137 0.000347 0.395878 6.9226e-01
## log(NewsCount) 0.000106 0.000102 1.040029 2.9853e-01
## log(Amihud_monthly) -0.002067 0.000150 -13.806484 < 2.2e-16 ***
## OMRFlag 0.001652 0.000297 5.570175 3.1240e-08 ***
## ... 10 coefficients remaining (display them with summary() or use argument n)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230779
## Within R2: 0.056923
fe_2sls_nocontrol <- feols(fml = RepIntensity ~ l(RepIntensity, 1)
| Ticker + time + Ticker^yearqtr(YM)| AIA ~ MarketAIA , # + Ticker^Year
data = pdShaRep,
cluster = c("Ticker"))
## NOTE: 12,149 observations removed because of NA values (RHS: 12,149).
fe_2slsC <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag +
l(RET_m1, 1:3)+ log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol)
| Ticker + time | AIAC ~ MarketAIAC , # + Ticker^yearqtr(YM)
data = pdShaRep,
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
fe_2slsC_nocontrol <- feols(fml = RepIntensity ~ 1
| Ticker + time | AIAC ~ MarketAIAC , # + Ticker^Year
data = pdShaRep,
cluster = c("Ticker"))
fe_2sls_vol <- feols(fml = RepIntensity_vol ~ l(RepIntensity_vol, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol)
| Ticker + time | AIA ~ MarketAIA , # + Ticker^Year
data = pdShaRep,
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
fe_2slsC_vol <- feols(fml = RepIntensity_vol ~ l(RepIntensity_vol, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + CtA + EBITDAtA + (Leverage) + log(BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + log(TradeVol)
| Ticker + time | AIAC ~ MarketAIAC , # + Ticker^Year
data = pdShaRep,
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
fe_2sls
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000564 0.000110 -5.132138 3.3273e-07 ***
## l(RepIntensity, 1) 0.175804 0.024377 7.211989 9.6171e-13 ***
## ANews -0.000525 0.000155 -3.379464 7.4907e-04 ***
## NewsAverage 0.000131 0.000347 0.377680 7.0573e-01
## log(NewsCount) 0.000111 0.000102 1.087722 2.7693e-01
## log(Amihud_monthly) -0.002069 0.000150 -13.818555 < 2.2e-16 ***
## OMRFlag 0.001651 0.000297 5.562898 3.2538e-08 ***
## l(RET_m1, 1) -0.004001 0.000478 -8.369848 < 2.2e-16 ***
## l(RET_m1, 2) -0.001564 0.000396 -3.944144 8.4615e-05 ***
## l(RET_m1, 3) -0.000573 0.000373 -1.535225 1.2499e-01
## log(TA) -0.000848 0.000421 -2.015178 4.4104e-02 *
## CtA 0.001771 0.000764 2.319118 2.0552e-02 *
## EBITDAtA 0.005004 0.002832 1.766994 7.7478e-02 .
## Leverage 0.000123 0.001460 0.084518 9.3266e-01
## log(BM) 0.000945 0.000139 6.816235 1.4614e-11 ***
## DivtoAsset -0.009282 0.017855 -0.519857 6.0326e-01
## l(log(MarCap), 1) -0.001178 0.000386 -3.049153 2.3440e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230749
## Within R2: 0.056862
## F-test (1st stage), AIA: stat = 211,915.5 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 2.08848, p = 0.148421, on 1 and 38,122 DoF.
fe_2slsC
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIAC, Instr.: MarketAIAC
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIAC -0.000770 0.000158 -4.890097 1.1414e-06 ***
## l(RepIntensity, 1) 0.175744 0.024373 7.210665 9.7073e-13 ***
## ANews -0.000524 0.000155 -3.367864 7.8100e-04 ***
## NewsAverage 0.000092 0.000347 0.265294 7.9083e-01
## log(NewsCount) 0.000114 0.000103 1.102129 2.7062e-01
## log(Amihud_monthly) -0.002080 0.000150 -13.868930 < 2.2e-16 ***
## OMRFlag 0.001647 0.000297 5.546434 3.5672e-08 ***
## l(RET_m1, 1) -0.003999 0.000478 -8.361985 < 2.2e-16 ***
## l(RET_m1, 2) -0.001575 0.000397 -3.969515 7.6195e-05 ***
## l(RET_m1, 3) -0.000586 0.000373 -1.571459 1.1633e-01
## log(TA) -0.000846 0.000421 -2.009772 4.4674e-02 *
## CtA 0.001768 0.000764 2.314926 2.0781e-02 *
## EBITDAtA 0.005010 0.002840 1.764187 7.7949e-02 .
## Leverage 0.000165 0.001459 0.113279 9.0983e-01
## log(BM) 0.000946 0.000139 6.826498 1.3642e-11 ***
## DivtoAsset -0.009600 0.017967 -0.534305 5.9323e-01
## l(log(MarCap), 1) -0.001171 0.000386 -3.033077 2.4715e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230792
## Within R2: 0.056915
## F-test (1st stage), AIAC: stat = 329,073.2 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 0.413423, p = 0.520241, on 1 and 38,122 DoF.
fe_2sls_vol
## TSLS estimation, Dep. Var.: RepIntensity_vol, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity_vol
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.004347 0.000595 -7.304378 4.9968e-13 ***
## l(RepIntensity_vol, 1) 0.202606 0.037375 5.420835 7.1355e-08 ***
## ANews -0.001068 0.000795 -1.343191 1.7946e-01
## NewsAverage -0.002160 0.001564 -1.381224 1.6746e-01
## log(NewsCount) -0.002138 0.000565 -3.784395 1.6151e-04 ***
## log(Amihud_monthly) -0.004551 0.000740 -6.152837 1.0283e-09 ***
## OMRFlag 0.007728 0.001614 4.789168 1.8786e-06 ***
## l(RET_m1, 1) -0.012152 0.001968 -6.174210 9.0207e-10 ***
## l(RET_m1, 2) -0.003264 0.001656 -1.971727 4.8865e-02 *
## l(RET_m1, 3) 0.000378 0.001634 0.231424 8.1702e-01
## log(TA) 0.000785 0.002000 0.392559 6.9471e-01
## CtA 0.013197 0.004004 3.296313 1.0076e-03 **
## EBITDAtA 0.009254 0.011940 0.775080 4.3844e-01
## Leverage -0.018019 0.007099 -2.538285 1.1262e-02 *
## log(BM) 0.003348 0.000680 4.924339 9.6184e-07 ***
## DivtoAsset -0.069913 0.108680 -0.643293 5.2015e-01
## l(log(MarCap), 1) -0.004200 0.001865 -2.251636 2.4521e-02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.028979 Adj. R2: 0.266196
## Within R2: 0.055661
## F-test (1st stage), AIA: stat = 211,905.7 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 7.56539, p = 0.005953, on 1 and 38,122 DoF.
fe_2slsC_vol
## TSLS estimation, Dep. Var.: RepIntensity_vol, Endo.: AIAC, Instr.: MarketAIAC
## Second stage: Dep. Var.: RepIntensity_vol
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIAC -0.006000 0.000831 -7.223320 8.8786e-13 ***
## l(RepIntensity_vol, 1) 0.202334 0.037366 5.414892 7.3709e-08 ***
## ANews -0.001053 0.000798 -1.318687 1.8752e-01
## NewsAverage -0.002441 0.001583 -1.542058 1.2332e-01
## log(NewsCount) -0.002117 0.000565 -3.745145 1.8864e-04 ***
## log(Amihud_monthly) -0.004639 0.000739 -6.274595 4.8497e-10 ***
## OMRFlag 0.007704 0.001615 4.770377 2.0592e-06 ***
## l(RET_m1, 1) -0.012137 0.001969 -6.162764 9.6767e-10 ***
## l(RET_m1, 2) -0.003353 0.001657 -2.023628 4.3225e-02 *
## l(RET_m1, 3) 0.000271 0.001635 0.165908 8.6826e-01
## log(TA) 0.000803 0.001998 0.401899 6.8783e-01
## CtA 0.013180 0.004005 3.290899 1.0271e-03 **
## EBITDAtA 0.009292 0.012010 0.773752 4.3923e-01
## Leverage -0.017683 0.007094 -2.492790 1.2805e-02 *
## log(BM) 0.003362 0.000679 4.950421 8.4367e-07 ***
## DivtoAsset -0.072413 0.109153 -0.663409 5.0719e-01
## l(log(MarCap), 1) -0.004141 0.001864 -2.221834 2.6476e-02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.028976 Adj. R2: 0.266357
## Within R2: 0.055868
## F-test (1st stage), AIAC: stat = 329,083.2 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 2.46053, p = 0.116747, on 1 and 38,122 DoF.
fitstat(fe_2sls, ~ cd + r2 + AIC + BIC + ivfall + ivwaldall + wh, cluster = 'Ticker')
## Warning in log(BM): NaNs produced
## Warning in log(BM): NaNs produced
## Cragg-Donald: 212,540.2
## R2: 0.256983
## AIC: -297,375.2
## BIC: -285,813.5
## F-test (IV only): stat = 49.4 , p = 2.075e-12, on 1 and 39,351 DoF.
## Wald (IV only): stat = 26.3 , p = 2.878e-7 , on 1 and 39,351 DoF, VCOV: Clustered (Ticker).
## Wu-Hausman: stat = 2.08848, p = 0.148421 , on 1 and 38,122 DoF.
fitstat(fe_2sls_vol, ~ r2 + ivfall + ivwaldall + wh, cluster = 'Ticker')
## R2: 0.29122
## F-test (IV only): stat = 102.1 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wald (IV only): stat = 53.4 , p = 2.838e-13, on 1 and 39,351 DoF, VCOV: Clustered (Ticker).
## Wu-Hausman: stat = 7.56539, p = 0.005953 , on 1 and 38,122 DoF.
if (0==1) {
fe_2sls <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
Amihud_monthly + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + CtA + EBITDAtA + log(Leverage) + (BM) + DivtoAsset +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1)# + log(TradeVol)
| Ticker + time | AIA ~ MarketAIA , # + Ticker^Year
data = pdShaRep,
cluster = c("Ticker"))
fitstat(fe_2sls, ~ r2 + AIC + BIC + ivfall + ivwaldall + wh, cluster = 'Ticker')
}
summary(fe_2sls, stage = 1)
## TSLS estimation, Dep. Var.: AIA, Endo.: AIA, Instr.: MarketAIA
## First stage: Dep. Var.: AIA
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA -1246.246377 5.213920 -239.022901 < 2.2e-16 ***
## l(RepIntensity, 1) -0.038911 0.108752 -0.357791 7.2056e-01
## ANews 0.012178 0.004044 3.011646 2.6515e-03 **
## NewsAverage -0.021503 0.008629 -2.492072 1.2831e-02 *
## log(NewsCount) 0.017287 0.002222 7.780849 1.5196e-14 ***
## log(Amihud_monthly) -0.006228 0.003398 -1.832821 6.7071e-02 .
## OMRFlag -0.005216 0.003510 -1.486078 1.3752e-01
## l(RET_m1, 1) -0.014092 0.008494 -1.659027 9.7366e-02 .
## l(RET_m1, 2) -0.005770 0.008700 -0.663160 5.0735e-01
## l(RET_m1, 3) 0.002708 0.011858 0.228379 8.1939e-01
## log(TA) 0.002704 0.011175 0.241974 8.0884e-01
## CtA -0.020015 0.017653 -1.133812 2.5709e-01
## EBITDAtA 0.045051 0.040707 1.106719 2.6863e-01
## Leverage -0.028830 0.041254 -0.698833 4.8479e-01
## log(BM) -0.000962 0.002774 -0.347000 7.2865e-01
## DivtoAsset -0.856941 0.631948 -1.356031 1.7534e-01
## l(log(MarCap), 1) -0.005619 0.008903 -0.631094 5.2810e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.146318 Adj. R2: 0.954451
## Within R2: 0.866112
## F-test (1st stage): stat = 211,915.5, p < 2.2e-16, on 1 and 39,351 DoF.
summary(fe_2sls, stage = 2)
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000564 0.000110 -5.132138 3.3273e-07 ***
## l(RepIntensity, 1) 0.175804 0.024377 7.211989 9.6171e-13 ***
## ANews -0.000525 0.000155 -3.379464 7.4907e-04 ***
## NewsAverage 0.000131 0.000347 0.377680 7.0573e-01
## log(NewsCount) 0.000111 0.000102 1.087722 2.7693e-01
## log(Amihud_monthly) -0.002069 0.000150 -13.818555 < 2.2e-16 ***
## OMRFlag 0.001651 0.000297 5.562898 3.2538e-08 ***
## l(RET_m1, 1) -0.004001 0.000478 -8.369848 < 2.2e-16 ***
## l(RET_m1, 2) -0.001564 0.000396 -3.944144 8.4615e-05 ***
## l(RET_m1, 3) -0.000573 0.000373 -1.535225 1.2499e-01
## log(TA) -0.000848 0.000421 -2.015178 4.4104e-02 *
## CtA 0.001771 0.000764 2.319118 2.0552e-02 *
## EBITDAtA 0.005004 0.002832 1.766994 7.7478e-02 .
## Leverage 0.000123 0.001460 0.084518 9.3266e-01
## log(BM) 0.000945 0.000139 6.816235 1.4614e-11 ***
## DivtoAsset -0.009282 0.017855 -0.519857 6.0326e-01
## l(log(MarCap), 1) -0.001178 0.000386 -3.049153 2.3440e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230749
## Within R2: 0.056862
## F-test (1st stage), AIA: stat = 211,915.5 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 2.08848, p = 0.148421, on 1 and 38,122 DoF.
etable(summary(fe_2sls, stage = 1),
summary(fe_2sls_vol, stage = 1),
summary(fe_2slsC, stage = 1),
summary(fe_2slsC_vol, stage = 1),
fitstat = ~ . + ivf1 + ivwaldall + wh + wh.p,
tex = T)
## \begingroup
## \centering
## \begin{tabular}{lcccc}
## \tabularnewline \midrule \midrule
## Dependent Variables: & \multicolumn{2}{c}{AIA} & \multicolumn{2}{c}{AIAC}\\
## Model: & (1) & (2) & (3) & (4)\\
## \midrule
## \emph{Variables}\\
## MarketAIA & -1,246.2$^{***}$ & -1,246.3$^{***}$ & & \\
## & (5.214) & (5.214) & & \\
## l(RepIntensity,1) & -0.0389 & & -0.0376 & \\
## & (0.1088) & & (0.0784) & \\
## ANews & 0.0122$^{***}$ & 0.0122$^{***}$ & 0.0082$^{***}$ & 0.0082$^{***}$\\
## & (0.0040) & (0.0040) & (0.0025) & (0.0025)\\
## NewsAverage & -0.0215$^{**}$ & -0.0215$^{**}$ & -0.0143$^{**}$ & -0.0142$^{**}$\\
## & (0.0086) & (0.0086) & (0.0055) & (0.0055)\\
## log(NewsCount) & 0.0173$^{***}$ & 0.0173$^{***}$ & 0.0123$^{***}$ & 0.0123$^{***}$\\
## & (0.0022) & (0.0022) & (0.0015) & (0.0015)\\
## log(Amihud\_monthly) & -0.0062$^{*}$ & -0.0062$^{*}$ & -0.0077$^{***}$ & -0.0077$^{***}$\\
## & (0.0034) & (0.0034) & (0.0022) & (0.0022)\\
## OMRFlag & -0.0052 & -0.0052 & -0.0045$^{*}$ & -0.0045$^{*}$\\
## & (0.0035) & (0.0035) & (0.0025) & (0.0025)\\
## l(RET\_m1,1) & -0.0141$^{*}$ & -0.0140$^{*}$ & -0.0103$^{**}$ & -0.0103$^{**}$\\
## & (0.0085) & (0.0085) & (0.0051) & (0.0051)\\
## l(RET\_m1,2) & -0.0058 & -0.0057 & -0.0056 & -0.0055\\
## & (0.0087) & (0.0087) & (0.0056) & (0.0056)\\
## l(RET\_m1,3) & 0.0027 & 0.0027 & -0.0013 & -0.0013\\
## & (0.0119) & (0.0118) & (0.0062) & (0.0062)\\
## log(TA) & 0.0027 & 0.0027 & 0.0043 & 0.0043\\
## & (0.0112) & (0.0112) & (0.0077) & (0.0077)\\
## CtA & -0.0200 & -0.0200 & -0.0168 & -0.0168\\
## & (0.0176) & (0.0177) & (0.0134) & (0.0134)\\
## EBITDAtA & 0.0450 & 0.0449 & 0.0352 & 0.0351\\
## & (0.0407) & (0.0407) & (0.0312) & (0.0311)\\
## Leverage & -0.0288 & -0.0289 & -0.0198 & -0.0199\\
## & (0.0413) & (0.0413) & (0.0272) & (0.0272)\\
## log(BM) & -0.0010 & -0.0010 & -0.0011 & -0.0011\\
## & (0.0028) & (0.0028) & (0.0022) & (0.0022)\\
## DivtoAsset & -0.8569 & -0.8568 & -0.5032 & -0.5030\\
## & (0.6319) & (0.6316) & (0.4763) & (0.4759)\\
## l(log(MarCap),1) & -0.0056 & -0.0056 & -0.0053 & -0.0053\\
## & (0.0089) & (0.0089) & (0.0061) & (0.0061)\\
## l(RepIntensity\_vol,1) & & -0.0094 & & -0.0093\\
## & & (0.0224) & & (0.0166)\\
## MarketAIAC & & & -1,234.8$^{***}$ & -1,234.9$^{***}$\\
## & & & (6.101) & (6.100)\\
## \midrule
## \emph{Fixed-effects}\\
## Ticker & Yes & Yes & Yes & Yes\\
## time & Yes & Yes & Yes & Yes\\
## \midrule
## \emph{Fit statistics}\\
## Observations & 39,470 & 39,470 & 39,470 & 39,470\\
## R$^2$ & 0.95600 & 0.95600 & 0.97451 & 0.97451\\
## Within R$^2$ & 0.86611 & 0.86611 & 0.90870 & 0.90870\\
## F-test (1st stage) & 211,915.5 & 211,905.7 & 329,073.2 & 329,083.2\\
## Wald (IV only) & 57,131.9 & 57,131.8 & 40,972.5 & 40,976.0\\
## \midrule \midrule
## \multicolumn{5}{l}{\emph{Clustered (Ticker) standard-errors in parentheses}}\\
## \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
## \end{tabular}
## \par\endgroup
etable(summary(fe_2sls, stage = 2),
summary(fe_2sls_vol, stage = 2),
summary(fe_2slsC, stage = 2),
summary(fe_2slsC_vol, stage = 2),
fitstat = ~ . + ivfall + ivwaldall + wh + wh.p,
tex = F)
## summary(fe_2sls, .. summary(fe_2sls_v..
## Dependent Var.: RepIntensity RepIntensity_vol
##
## AIA -0.0006*** (0.0001) -0.0043*** (0.0006)
## l(RepIntensity,1) 0.1758*** (0.0244)
## ANews -0.0005*** (0.0002) -0.0011 (0.0008)
## NewsAverage 0.0001 (0.0003) -0.0022 (0.0016)
## log(NewsCount) 0.0001 (0.0001) -0.0021*** (0.0006)
## log(Amihud_monthly) -0.0021*** (0.0001) -0.0046*** (0.0007)
## OMRFlag 0.0017*** (0.0003) 0.0077*** (0.0016)
## l(RET_m1,1) -0.0040*** (0.0005) -0.0121*** (0.0020)
## l(RET_m1,2) -0.0016*** (0.0004) -0.0033* (0.0017)
## l(RET_m1,3) -0.0006 (0.0004) 0.0004 (0.0016)
## log(TA) -0.0008* (0.0004) 0.0008 (0.0020)
## CtA 0.0018* (0.0008) 0.0132** (0.0040)
## EBITDAtA 0.0050. (0.0028) 0.0093 (0.0119)
## Leverage 0.0001 (0.0015) -0.0180* (0.0071)
## log(BM) 0.0009*** (0.0001) 0.0033*** (0.0007)
## DivtoAsset -0.0093 (0.0179) -0.0699 (0.1087)
## l(log(MarCap),1) -0.0012** (0.0004) -0.0042* (0.0019)
## l(RepIntensity_vol,1) 0.2026*** (0.0374)
## AIAC
## Fixed-Effects: ------------------- -------------------
## Ticker Yes Yes
## time Yes Yes
## _____________________ ___________________ ___________________
## S.E.: Clustered by: Ticker by: Ticker
## Observations 39,470 39,470
## R2 0.25698 0.29122
## Within R2 0.05686 0.05566
## F-test (IV only) 49.443 102.10
## Wald (IV only) 26.339 53.354
## Wu-Hausman 2.0885 7.5654
## Wu-Hausman, p-value 0.14842 0.00595
##
## summary(fe_2slsC,.. summary(fe_2slsC_..
## Dependent Var.: RepIntensity RepIntensity_vol
##
## AIA
## l(RepIntensity,1) 0.1757*** (0.0244)
## ANews -0.0005*** (0.0002) -0.0011 (0.0008)
## NewsAverage 9.2e-5 (0.0003) -0.0024 (0.0016)
## log(NewsCount) 0.0001 (0.0001) -0.0021*** (0.0006)
## log(Amihud_monthly) -0.0021*** (0.0001) -0.0046*** (0.0007)
## OMRFlag 0.0016*** (0.0003) 0.0077*** (0.0016)
## l(RET_m1,1) -0.0040*** (0.0005) -0.0121*** (0.0020)
## l(RET_m1,2) -0.0016*** (0.0004) -0.0034* (0.0017)
## l(RET_m1,3) -0.0006 (0.0004) 0.0003 (0.0016)
## log(TA) -0.0008* (0.0004) 0.0008 (0.0020)
## CtA 0.0018* (0.0008) 0.0132** (0.0040)
## EBITDAtA 0.0050. (0.0028) 0.0093 (0.0120)
## Leverage 0.0002 (0.0015) -0.0177* (0.0071)
## log(BM) 0.0009*** (0.0001) 0.0034*** (0.0007)
## DivtoAsset -0.0096 (0.0180) -0.0724 (0.1092)
## l(log(MarCap),1) -0.0012** (0.0004) -0.0041* (0.0019)
## l(RepIntensity_vol,1) 0.2023*** (0.0374)
## AIAC -0.0008*** (0.0002) -0.0060*** (0.0008)
## Fixed-Effects: ------------------- -------------------
## Ticker Yes Yes
## time Yes Yes
## _____________________ ___________________ ___________________
## S.E.: Clustered by: Ticker by: Ticker
## Observations 39,470 39,470
## R2 0.25702 0.29138
## Within R2 0.05692 0.05587
## F-test (IV only) 48.645 102.73
## Wald (IV only) 23.913 52.176
## Wu-Hausman 0.41342 2.4605
## Wu-Hausman, p-value 0.52024 0.11675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
feols(fml = RepIntensity ~ AIA + l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + NewsCount +
Amihud_monthly + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + log(CtA) + (EBITDAtA) + log(Leverage) + (BM) + DivtoAsset +
l(log(MarCap), 1) + d(Volatility,1) + l(Volatility, 1) + log(TradeVol)
| Ticker + time , # + Ticker^Year
data = pdShaRep,
cluster = c("Ticker")) %>%
summary
## NOTE: 32,741 observations removed because of NA and infinite values (RHS: 32,741).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 41,044
## Fixed-effects: Ticker: 1,251, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.000643483 0.000098935 -6.504086 1.1277e-10 ***
## l(RepIntensity, 1) 0.174439322 0.023335328 7.475332 1.4433e-13 ***
## ANews -0.000511469 0.000165684 -3.087018 2.0662e-03 **
## NewsAverage 0.000302765 0.000384873 0.786663 4.3163e-01
## NewsCount -0.000000228 0.000000310 -0.732900 4.6376e-01
## Amihud_monthly 0.011311045 0.008981598 1.259358 2.0814e-01
## OMRFlag 0.001579378 0.000282518 5.590366 2.7796e-08 ***
## l(RET_m1, 1) -0.002875966 0.000460414 -6.246475 5.7440e-10 ***
## l(RET_m1, 2) -0.001380887 0.000394502 -3.500329 4.8104e-04 ***
## l(RET_m1, 3) -0.000713748 0.000368079 -1.939115 5.2712e-02 .
## log(TA) -0.000733340 0.000276439 -2.652808 8.0836e-03 **
## log(CtA) 0.000236589 0.000067028 3.529687 4.3116e-04 ***
## EBITDAtA 0.005574247 0.002665316 2.091402 3.6693e-02 *
## log(Leverage) 0.000572836 0.000274811 2.084471 3.7320e-02 *
## BM 0.000167625 0.000207246 0.808825 4.1877e-01
## DivtoAsset -0.028233974 0.020976721 -1.345967 1.7856e-01
## l(log(MarCap), 1) 0.000367131 0.000237969 1.542769 1.2314e-01
## d(Volatility, 1) -0.043794605 0.007211907 -6.072542 1.6677e-09 ***
## l(Volatility, 1) -0.046947723 0.008905457 -5.271793 1.5903e-07 ***
## log(TradeVol) 0.001996389 0.000167365 11.928383 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005474 Adj. R2: 0.226266
## Within R2: 0.052337
feols(fml = RepIntensity ~ MarketAIA + l(RepIntensity, 1)
| Ticker + time, data = pdShaRep, cluster = "Ticker")
## NOTE: 12,149 observations removed because of NA values (RHS: 12,149).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 61,636
## Fixed-effects: Ticker: 1,526, time: 128
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA 0.621234 0.108842 5.70764 1.3742e-08 ***
## l(RepIntensity, 1) 0.187119 0.020638 9.06674 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005846 Adj. R2: 0.206696
## Within R2: 0.034047
feglm(fml = ShaRepYes ~ AIA + l(ShaRepYes, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + NewsCount +
Amihud_monthly + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + CtA + EBITDAtA + log(Leverage) + (BM) + DivtoAsset +
l(log(MarCap), 1) + d(Volatility,1) + l(Volatility, 1) + log(TradeVol)
| Ticker + time , # + Ticker^Year
data = pdShaRep, family = binomial,
cluster = c("Ticker"))
## NOTES: 32,698 observations removed because of NA values (RHS: 32,698).
## 328/0 fixed-effects (4,666 observations) removed because of only 0 (or only 1) outcomes.
## GLM estimation, family = binomial, Dep. Var.: ShaRepYes
## Observations: 36,421
## Fixed-effects: Ticker: 923, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.339306 0.054271 -6.252085 4.0501e-10 ***
## l(ShaRepYes, 1) 1.891207 0.066513 28.433574 < 2.2e-16 ***
## ANews -0.210979 0.076097 -2.772506 5.5626e-03 **
## NewsAverage 0.264554 0.180525 1.465468 1.4279e-01
## NewsCount -0.000172 0.000190 -0.903192 3.6642e-01
## Amihud_monthly 14.826788 11.187188 1.325337 1.8506e-01
## OMRFlag 0.602179 0.125324 4.804962 1.5478e-06 ***
## l(RET_m1, 1) -1.283040 0.227131 -5.648901 1.6148e-08 ***
## ... 12 coefficients remaining (display them with summary() or use argument n)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Log-Likelihood: -15,260.9 Adj. Pseudo R2: 0.344347
## BIC: 41,486.9 Squared Cor.: 0.451475
## 5. within-firm standard deviation ----
fe_2sls_log <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | AIA ~ MarketAIA,
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:l(1-ShaRepYes, 1, fill = 0) + AIA:l(ShaRepYes, 1, fill = 0) ~ MarketAIA:l(1-ShaRepYes, 1, fill = 0) + MarketAIA:l(ShaRepYes, 1, fill = 0) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
summary(fe_2sls_log, stage = 2)
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000564 0.000110 -5.132138 3.3273e-07 ***
## l(RepIntensity, 1) 0.175804 0.024377 7.211989 9.6171e-13 ***
## ANews -0.000525 0.000155 -3.379464 7.4907e-04 ***
## NewsAverage 0.000131 0.000347 0.377680 7.0573e-01
## log(NewsCount) 0.000111 0.000102 1.087722 2.7693e-01
## log(Amihud_monthly) -0.002069 0.000150 -13.818555 < 2.2e-16 ***
## OMRFlag 0.001651 0.000297 5.562898 3.2538e-08 ***
## l(RET_m1, 1) -0.004001 0.000478 -8.369848 < 2.2e-16 ***
## l(RET_m1, 2) -0.001564 0.000396 -3.944144 8.4615e-05 ***
## l(RET_m1, 3) -0.000573 0.000373 -1.535225 1.2499e-01
## log(TA) -0.000848 0.000421 -2.015178 4.4104e-02 *
## CtA 0.001771 0.000764 2.319118 2.0552e-02 *
## EBITDAtA 0.005004 0.002832 1.766994 7.7478e-02 .
## Leverage 0.000123 0.001460 0.084518 9.3266e-01
## log(BM) 0.000945 0.000139 6.816235 1.4614e-11 ***
## DivtoAsset -0.009282 0.017855 -0.519857 6.0326e-01
## l(log(MarCap), 1) -0.001178 0.000386 -3.049153 2.3440e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230749
## Within R2: 0.056862
## F-test (1st stage), AIA: stat = 211,915.5 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 2.08848, p = 0.148421, on 1 and 38,122 DoF.
# iplot(fe_2sls_log)
fe_2sls_log2 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | AIA ~ MarketAIA , # + Ticker^yearqtr(YM)
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
#### 5.1 within-firm standard deviation ----
mm <- feols(fml = # AIA ~ 1
AIA ~ 1 # log(Amihud_monthly)
| Ticker + time, # + Ticker^year(YM),
data = pdShaRep, # [pdShaRep$ShaRepYes == T,],
cluster = c("Ticker"))
sqrt(sum((mm$residuals)^2) / (nrow(pdShaRep) - length(unique(pdShaRep$Ticker))))
## [1] 0.4251317
## 6. Logit model ----
fe_prob <- feglm(fml = ShaRepYes ~ AIA + l(ShaRepYes, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) # + log(TradeVol) # + lambda
| Ticker + time, # | AIA ~ MarketAIA , # + Ticker^yearqtr(YM)
data = pdShaRep, family = gaussian(link = "identity"), #binomial(link = 'logit'),
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
fe_prob
## GLM estimation, family = gaussian(link = "identity"), Dep. Var.: ShaRepYes
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA -0.032552 0.006132 -5.30881 1.3085e-07 ***
## l(ShaRepYes, 1) 0.365259 0.012394 29.47160 < 2.2e-16 ***
## ANews -0.022815 0.008917 -2.55862 1.0628e-02 *
## NewsAverage 0.046772 0.017975 2.60198 9.3803e-03 **
## log(NewsCount) -0.009222 0.006198 -1.48782 1.3705e-01
## log(Amihud_monthly) -0.056668 0.008076 -7.01674 3.7449e-12 ***
## OMRFlag 0.081345 0.014667 5.54597 3.5764e-08 ***
## l(RET_m1, 1) -0.147712 0.025826 -5.71955 1.3403e-08 ***
## ... 9 coefficients remaining (display them with summary() or use argument n)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Log-Likelihood: -14,557.5 Adj. Pseudo R2: 0.442167
## BIC: 43,370.6 Squared Cor.: 0.50686
fe_probC <- feglm(fml = ShaRepYes ~ AIAC + l(ShaRepYes, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) # + log(TradeVol) # + lambda
| Ticker + time, # | AIA ~ MarketAIA , # + Ticker^yearqtr(YM)
data = pdShaRep, family = gaussian(link = "identity"), #binomial(link = 'logit'),
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
fe_prob_2sls <- feols(fml = ShaRepYes ~ l(ShaRepYes, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) # + log(TradeVol) # + lambda
| Ticker + time | AIA ~ MarketAIA , # + Ticker^yearqtr(YM)
data = pdShaRep, #binomial(link = 'logit'),
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
fe_probC_2sls <- feols(fml = ShaRepYes ~ l(ShaRepYes, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) # + log(TradeVol) # + lambda
| Ticker + time | AIAC ~ MarketAIAC , # + Ticker^yearqtr(YM)
data = pdShaRep,
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
etable(fe_prob, fe_probC,
summary(fe_prob_2sls, stage = 2),
summary(fe_probC_2sls, stage = 2),
fitstat = ~ . + ivfall + ivwaldall + wh + wh.p,
tex = T)
## \begingroup
## \centering
## \begin{tabular}{lcccc}
## \tabularnewline \midrule \midrule
## Dependent Variable: & \multicolumn{4}{c}{ShaRepYes}\\
## Model: & (1) & (2) & (3) & (4)\\
## & gaussian("identity") & gaussian("identity") & OLS & OLS\\
## \midrule
## \emph{Variables}\\
## AIA & -0.0326$^{***}$ & & -0.0341$^{***}$ & \\
## & (0.0061) & & (0.0070) & \\
## l(ShaRepYes,1) & 0.3653$^{***}$ & 0.3651$^{***}$ & 0.3653$^{***}$ & 0.3651$^{***}$\\
## & (0.0124) & (0.0124) & (0.0124) & (0.0124)\\
## ANews & -0.0228$^{**}$ & -0.0227$^{**}$ & -0.0227$^{**}$ & -0.0226$^{**}$\\
## & (0.0089) & (0.0089) & (0.0089) & (0.0089)\\
## NewsAverage & 0.0468$^{***}$ & 0.0447$^{**}$ & 0.0475$^{***}$ & 0.0449$^{**}$\\
## & (0.0180) & (0.0179) & (0.0179) & (0.0179)\\
## log(NewsCount) & -0.0092 & -0.0090 & -0.0090 & -0.0090\\
## & (0.0062) & (0.0062) & (0.0062) & (0.0062)\\
## log(Amihud\_monthly) & -0.0567$^{***}$ & -0.0573$^{***}$ & -0.0567$^{***}$ & -0.0573$^{***}$\\
## & (0.0081) & (0.0081) & (0.0081) & (0.0081)\\
## OMRFlag & 0.0813$^{***}$ & 0.0812$^{***}$ & 0.0814$^{***}$ & 0.0812$^{***}$\\
## & (0.0147) & (0.0147) & (0.0147) & (0.0147)\\
## l(RET\_m1,1) & -0.1477$^{***}$ & -0.1476$^{***}$ & -0.1479$^{***}$ & -0.1477$^{***}$\\
## & (0.0258) & (0.0258) & (0.0258) & (0.0258)\\
## l(RET\_m1,2) & -0.0553$^{**}$ & -0.0560$^{***}$ & -0.0554$^{**}$ & -0.0560$^{***}$\\
## & (0.0216) & (0.0216) & (0.0216) & (0.0216)\\
## l(RET\_m1,3) & -0.0338 & -0.0347$^{*}$ & -0.0339 & -0.0347$^{*}$\\
## & (0.0210) & (0.0210) & (0.0210) & (0.0210)\\
## log(TA) & -0.0896$^{***}$ & -0.0895$^{***}$ & -0.0896$^{***}$ & -0.0895$^{***}$\\
## & (0.0290) & (0.0290) & (0.0290) & (0.0290)\\
## CtA & -0.0384 & -0.0386 & -0.0382 & -0.0385\\
## & (0.0516) & (0.0516) & (0.0516) & (0.0516)\\
## EBITDAtA & 0.3243 & 0.3246 & 0.3239 & 0.3245\\
## & (0.2363) & (0.2371) & (0.2364) & (0.2371)\\
## Leverage & -0.0718 & -0.0692 & -0.0714 & -0.0690\\
## & (0.0932) & (0.0932) & (0.0932) & (0.0933)\\
## log(BM) & 0.0383$^{***}$ & 0.0384$^{***}$ & 0.0384$^{***}$ & 0.0384$^{***}$\\
## & (0.0091) & (0.0091) & (0.0091) & (0.0091)\\
## DivtoAsset & 0.1992 & 0.1801 & 0.1981 & 0.1792\\
## & (1.602) & (1.607) & (1.602) & (1.607)\\
## l(log(MarCap),1) & 0.0358 & 0.0363 & 0.0360 & 0.0363\\
## & (0.0239) & (0.0239) & (0.0239) & (0.0239)\\
## AIAC & & -0.0451$^{***}$ & & -0.0460$^{***}$\\
## & & (0.0091) & & (0.0098)\\
## \midrule
## \emph{Fixed-effects}\\
## Ticker & Yes & Yes & Yes & Yes\\
## time & Yes & Yes & Yes & Yes\\
## \midrule
## \emph{Fit statistics}\\
## Observations & 39,470 & 39,470 & 39,470 & 39,470\\
## Squared Correlation & 0.50686 & 0.50683 & 0.50682 & 0.50680\\
## Pseudo R$^2$ & 0.48938 & 0.48934 & 0.48939 & 0.48936\\
## BIC & 43,370.6 & 43,372.7 & 43,368.7 & 43,370.8\\
## F-test (IV only) & & & 43.054 & 41.382\\
## Wald (IV only) & & & 23.945 & 22.127\\
## Wu-Hausman & & & 0.53937 & 0.15367\\
## Wu-Hausman, p-value & & & 0.46270 & 0.69506\\
## \midrule \midrule
## \multicolumn{5}{l}{\emph{Clustered (Ticker) standard-errors in parentheses}}\\
## \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
## \end{tabular}
## \par\endgroup
## 7. subgroup analysis -----
pdShaRep <- panel(ShaRep_AIA_merge_illiquidity_OMR_CCM_c2_summary %>%
# mutate(reporting_ym = as.yearmon(reporting),
# filing_ym = as.yearmon(filing)) %>%
filter(RepIntensity <= 0.10) %>%
mutate(TradeVol_shaout = TradeVol / ShaOut) %>%
group_by(YM) %>%
mutate(BM_q1 = quantile(BM, na.rm = T)[2],
BM_q2 = quantile(BM, na.rm = T)[3],
BM_q3 = quantile(BM, na.rm = T)[4],
BM_q4 = quantile(BM, na.rm = T)[5],
RET_md = median(RET_m1, na.rm = T),
RET_mean = mean(RET_m1, na.rm = T),
frequency = mean(ShaRepYes, na.rm = T),
Volatility_md = median(Volatility, na.rm = T)
) %>%
ungroup() %>%
mutate(UnderPriced = BM > BM_q2,
BM_middle = (BM >= BM_q1) & (BM <= BM_q3) ,
PriceQ1 = 1*(BM <= BM_q1),
PriceQ2 = 2*(BM > BM_q1 & BM <= BM_q2),
PriceQ3 = 3*(BM > BM_q2 & BM <= BM_q3),
PriceQ4 = 4*(BM > BM_q3 & BM <= BM_q4),
PriceQ = PriceQ1 + PriceQ2 + PriceQ3 + PriceQ4,
HighVolatility = Volatility > Volatility_md,
RET_m1_0 = RET_m1 > 0,
) %>%
mutate(YQ = yearqtr(YM))# %>%
#mutate(group1 = (wv_bhar_reb1 > 0) + (wv_bhar_reb0 > 0),
# group3 = (wv_bhar_reb3 > 0) + (wv_bhar_reb0 > 0),
# group6 = (wv_bhar_reb6 > 0) + (wv_bhar_reb0 > 0)
# )
, ~ Ticker + time)
names(pdShaRep)
## [1] "YM" "AIAC" "ANews" "NewsAverage"
## [5] "NewsCount" "MarketAIA" "MarketAIAC" "CUSIP"
## [9] "CIK" "gvkey" "Name" "cusip8"
## [13] "CUSIP_tts" "AIA" "ShaRep3" "missing3"
## [17] "Price2" "Amihud_monthly" "OMRFlag" "Ticker"
## [21] "RET_m1" "Volatility" "AvePrice" "TradeVol"
## [25] "TradeVolDollar" "MarCap" "ShaOut" "ShaOutPrevious"
## [29] "datadate" "tic" "cusip" "atq"
## [33] "ceqq" "cheq" "oibdpq" "pstkq"
## [37] "seqq" "txditcq" "cdvcy" "costat"
## [41] "dlcq" "dlttq" "BE" "TD"
## [45] "CtA" "TA" "Year" "Quarter"
## [49] "Month" "RepIntensity" "RepIntensity_vol" "ShaRepYes"
## [53] "BM" "DivtoAsset" "EBITDA" "Leverage"
## [57] "TradeVol_scaled" "EBITDAtA" "time" "sic"
## [61] "shortintadj" "OMR_YM" "ProgramMonth" "ShortInterest"
## [65] "wv_bhar_reb3" "wv_bhar_nreb3" "wv_bhar_reb6" "wv_bhar_nreb6"
## [69] "wv_bhar_reb1" "wv_bhar_nreb1" "wv_bhar_reb0" "wv_bhar_nreb0"
## [73] "TradeVol_shaout" "BM_q1" "BM_q2" "BM_q3"
## [77] "BM_q4" "RET_md" "RET_mean" "frequency"
## [81] "Volatility_md" "UnderPriced" "BM_middle" "PriceQ1"
## [85] "PriceQ2" "PriceQ3" "PriceQ4" "PriceQ"
## [89] "HighVolatility" "RET_m1_0" "YQ"
fe_2sls_underpriced <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(l(UnderPriced, 1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + l(log(BM), 1) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
AIA:i(l(UnderPriced, 1)) ~ MarketAIA:i(l(UnderPriced, 1)), ## UnderPriced versus Overpriced
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = 0) + AIA:l(ShaRepYes, 1, fill = 0) ~ MarketAIA:l(1-ShaRepYes, 1, fill = 0) + MarketAIA:l(ShaRepYes, 1, fill = 0) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,312 observations removed because of NA and infinite values (RHS: 34,312, IV: 12,591/12,591).
fe_2sls_underpriced
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(l(UnderPriced, 1)), Instr.: MarketAIA:i(l(UnderPriced, 1))
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,473
## Fixed-effects: Ticker: 1,230, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA:l(UnderPriced, 1)::FALSE -0.000255 0.000259 -0.983928 3.2534e-01
## fit_AIA:l(UnderPriced, 1)::TRUE -0.000860 0.000258 -3.328963 8.9754e-04 ***
## l(RepIntensity, 1) 0.174519 0.024252 7.196134 1.0748e-12 ***
## l(UnderPriced, 1)::TRUE 0.000863 0.000371 2.323916 2.0292e-02 *
## ANews -0.000529 0.000154 -3.444221 5.9200e-04 ***
## NewsAverage 0.000057 0.000338 0.169977 8.6506e-01
## log(NewsCount) 0.000128 0.000104 1.230235 2.1884e-01
## log(Amihud_monthly) -0.002050 0.000148 -13.810363 < 2.2e-16 ***
## OMRFlag 0.001638 0.000298 5.491618 4.8353e-08 ***
## l(RET_m1, 1) -0.004426 0.000486 -9.108216 < 2.2e-16 ***
## l(RET_m1, 2) -0.001577 0.000398 -3.959045 7.9565e-05 ***
## l(RET_m1, 3) -0.000566 0.000374 -1.511670 1.3088e-01
## log(TA) -0.000563 0.000421 -1.337008 1.8147e-01
## CtA 0.001840 0.000759 2.424386 1.5478e-02 *
## EBITDAtA 0.005006 0.002819 1.775733 7.6024e-02 .
## Leverage -0.000513 0.001472 -0.348733 7.2735e-01
## l(log(BM), 1) 0.000608 0.000132 4.605769 4.5367e-06 ***
## DivtoAsset -0.011642 0.018233 -0.638535 5.2324e-01
## l(log(MarCap), 1) -0.001397 0.000384 -3.643520 2.8015e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005418 Adj. R2: 0.229254
## Within R2: 0.055888
## F-test (1st stage), AIA:l(UnderPriced, 1)::FALSE: stat = 11,775.9 , p < 2.2e-16 , on 2 and 39,352 DoF.
## F-test (1st stage), AIA:l(UnderPriced, 1)::TRUE : stat = 10,488.1 , p < 2.2e-16 , on 2 and 39,352 DoF.
## Wu-Hausman: stat = 1.673, p = 0.187697, on 2 and 38,121 DoF.
{
fe_2sls_underpriced2 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + (l(UnderPriced, 1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + l(log(BM), 1) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
AIA + AIA:(l(UnderPriced, 1)) ~ MarketAIA + MarketAIA:(l(UnderPriced, 1)), ## UnderPriced versus Overpriced
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = 0) + AIA:l(ShaRepYes, 1, fill = 0) ~ MarketAIA:l(1-ShaRepYes, 1, fill = 0) + MarketAIA:l(ShaRepYes, 1, fill = 0) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
fe_2sls_underpriced2
fe_2sls_underpriced3 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + (l(BM_middle, 1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + l(log(BM), 1) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
AIA:(l(BM_middle, 1)) ~ MarketAIA:(l(BM_middle, 1)), ## UnderPriced versus Overpriced
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = 0) + AIA:l(ShaRepYes, 1, fill = 0) ~ MarketAIA:l(1-ShaRepYes, 1, fill = 0) + MarketAIA:l(ShaRepYes, 1, fill = 0) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
fe_2sls_underpriced3
pdShaRep %>% group_by(UnderPriced %in% c(T, F)) %>% summarise(RepFrequency = mean(ShaRepYes, na.rm = T))
feols(fml = ShaRep3 ~ i(UnderPriced) | Ticker + time, data = pdShaRep)
pdShaRep %>% group_by((ShaRepYes)) %>% summarise(RepFrequency = mean(ShaRepYes, na.rm = T))
feols(fml = ShaRep3 ~ i(l(ShaRepYes, 1, fill = NA)) | Ticker + time, data = pdShaRep)
}
## Warning in log(BM): NaNs produced
## NOTE: 34,312 observations removed because of NA and infinite values (RHS: 34,312, IV: 12,591/12,591).
## Warning in log(BM): NaNs produced
## NOTE: 34,312 observations removed because of NA and infinite values (RHS: 34,312, IV: 12,591/12,591).
## NOTE: 540 observations removed because of NA values (RHS: 540).
## NOTE: 12,149 observations removed because of NA values (RHS: 12,149).
## OLS estimation, Dep. Var.: ShaRep3
## Observations: 61,636
## Fixed-effects: Ticker: 1,526, time: 128
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## l(ShaRepYes, 1, fill = NA)::1 0.617227 0.099553 6.20001 7.2537e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 3.44117 Adj. R2: 0.363998
## Within R2: 0.004757
fe_2sls_sharepprevious <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(l(ShaRepYes, 1, fill = NA)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
AIA:i(l(ShaRepYes, 1, fill = NA)) ~ MarketAIA:i(l(ShaRepYes, 1, fill = NA)) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315, IV: 12,149/12,149).
fe_2sls_sharepprevious
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(l(ShaRepYes, 1, fill = NA)), Instr.: MarketAIA:i(l(ShaRepYes, 1, fill = NA))
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value
## fit_AIA:l(ShaRepYes, 1, fill = NA)::0 -0.000764 0.000246 -3.098300
## fit_AIA:l(ShaRepYes, 1, fill = NA)::1 -0.000427 0.000209 -2.041947
## l(RepIntensity, 1) 0.151612 0.025979 5.836051
## l(ShaRepYes, 1, fill = NA)::1 0.000625 0.000317 1.972282
## ANews -0.000523 0.000158 -3.315550
## NewsAverage 0.000108 0.000354 0.305681
## log(NewsCount) 0.000153 0.000101 1.508543
## log(Amihud_monthly) -0.002080 0.000150 -13.871720
## OMRFlag 0.001608 0.000297 5.421542
## l(RET_m1, 1) -0.003949 0.000473 -8.341176
## l(RET_m1, 2) -0.001487 0.000395 -3.761115
## l(RET_m1, 3) -0.000523 0.000372 -1.408898
## log(TA) -0.000767 0.000412 -1.861296
## CtA 0.001914 0.000753 2.540355
## EBITDAtA 0.004457 0.002761 1.614532
## Leverage 0.000242 0.001432 0.168886
## log(BM) 0.000918 0.000137 6.700045
## DivtoAsset -0.009011 0.017564 -0.513032
## l(log(MarCap), 1) -0.001277 0.000385 -3.319349
## Pr(>|t|)
## fit_AIA:l(ShaRepYes, 1, fill = NA)::0 1.9905e-03 **
## fit_AIA:l(ShaRepYes, 1, fill = NA)::1 4.1370e-02 *
## l(RepIntensity, 1) 6.8320e-09 ***
## l(ShaRepYes, 1, fill = NA)::1 4.8801e-02 *
## ANews 9.4135e-04 ***
## NewsAverage 7.5990e-01
## log(NewsCount) 1.3167e-01
## log(Amihud_monthly) < 2.2e-16 ***
## OMRFlag 7.1080e-08 ***
## l(RET_m1, 1) < 2.2e-16 ***
## l(RET_m1, 2) 1.7713e-04 ***
## l(RET_m1, 3) 1.5912e-01
## log(TA) 6.2941e-02 .
## CtA 1.1196e-02 *
## EBITDAtA 1.0667e-01
## Leverage 8.6591e-01
## log(BM) 3.1657e-11 ***
## DivtoAsset 6.0802e-01
## l(log(MarCap), 1) 9.2875e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005399 Adj. R2: 0.232913
## Within R2: 0.059564
## F-test (1st stage), AIA:l(ShaRepYes, 1, fill = NA)::0: stat = 5,956.3 , p < 2.2e-16, on 2 and 39,349 DoF.
## F-test (1st stage), AIA:l(ShaRepYes, 1, fill = NA)::1: stat = 12,407.4 , p < 2.2e-16, on 2 and 39,349 DoF.
## Wu-Hausman: stat = 1.5813, p = 0.20572, on 2 and 38,119 DoF.
{
feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(l(ShaRepYes, 1, fill = NA)) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
AIA:i(l(ShaRepYes, 1, fill = NA)) ~ MarketAIA:i(l(ShaRepYes, 1, fill = NA)) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
}
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315, IV: 12,149/12,149).
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(l(ShaRepYes, 1, fill = NA)), Instr.: MarketAIA:i(l(ShaRepYes, 1, fill = NA))
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value
## fit_AIA:l(ShaRepYes, 1, fill = NA)::0 -0.000764 0.000246 -3.098300
## fit_AIA:l(ShaRepYes, 1, fill = NA)::1 -0.000427 0.000209 -2.041947
## l(RepIntensity, 1) 0.151612 0.025979 5.836051
## l(ShaRepYes, 1, fill = NA)::1 0.000625 0.000317 1.972282
## ANews -0.000523 0.000158 -3.315550
## NewsAverage 0.000108 0.000354 0.305681
## log(NewsCount) 0.000153 0.000101 1.508543
## log(Amihud_monthly) -0.002080 0.000150 -13.871720
## OMRFlag 0.001608 0.000297 5.421542
## l(RET_m1, 1) -0.003949 0.000473 -8.341176
## l(RET_m1, 2) -0.001487 0.000395 -3.761115
## l(RET_m1, 3) -0.000523 0.000372 -1.408898
## log(TA) -0.000767 0.000412 -1.861296
## CtA 0.001914 0.000753 2.540355
## EBITDAtA 0.004457 0.002761 1.614532
## Leverage 0.000242 0.001432 0.168886
## log(BM) 0.000918 0.000137 6.700045
## DivtoAsset -0.009011 0.017564 -0.513032
## l(log(MarCap), 1) -0.001277 0.000385 -3.319349
## Pr(>|t|)
## fit_AIA:l(ShaRepYes, 1, fill = NA)::0 1.9905e-03 **
## fit_AIA:l(ShaRepYes, 1, fill = NA)::1 4.1370e-02 *
## l(RepIntensity, 1) 6.8320e-09 ***
## l(ShaRepYes, 1, fill = NA)::1 4.8801e-02 *
## ANews 9.4135e-04 ***
## NewsAverage 7.5990e-01
## log(NewsCount) 1.3167e-01
## log(Amihud_monthly) < 2.2e-16 ***
## OMRFlag 7.1080e-08 ***
## l(RET_m1, 1) < 2.2e-16 ***
## l(RET_m1, 2) 1.7713e-04 ***
## l(RET_m1, 3) 1.5912e-01
## log(TA) 6.2941e-02 .
## CtA 1.1196e-02 *
## EBITDAtA 1.0667e-01
## Leverage 8.6591e-01
## log(BM) 3.1657e-11 ***
## DivtoAsset 6.0802e-01
## l(log(MarCap), 1) 9.2875e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005399 Adj. R2: 0.232913
## Within R2: 0.059564
## F-test (1st stage), AIA:l(ShaRepYes, 1, fill = NA)::0: stat = 5,956.3 , p < 2.2e-16, on 2 and 39,349 DoF.
## F-test (1st stage), AIA:l(ShaRepYes, 1, fill = NA)::1: stat = 12,407.4 , p < 2.2e-16, on 2 and 39,349 DoF.
## Wu-Hausman: stat = 1.5813, p = 0.20572, on 2 and 38,119 DoF.
fe_2sls_sharepprevious
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(l(ShaRepYes, 1, fill = NA)), Instr.: MarketAIA:i(l(ShaRepYes, 1, fill = NA))
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value
## fit_AIA:l(ShaRepYes, 1, fill = NA)::0 -0.000764 0.000246 -3.098300
## fit_AIA:l(ShaRepYes, 1, fill = NA)::1 -0.000427 0.000209 -2.041947
## l(RepIntensity, 1) 0.151612 0.025979 5.836051
## l(ShaRepYes, 1, fill = NA)::1 0.000625 0.000317 1.972282
## ANews -0.000523 0.000158 -3.315550
## NewsAverage 0.000108 0.000354 0.305681
## log(NewsCount) 0.000153 0.000101 1.508543
## log(Amihud_monthly) -0.002080 0.000150 -13.871720
## OMRFlag 0.001608 0.000297 5.421542
## l(RET_m1, 1) -0.003949 0.000473 -8.341176
## l(RET_m1, 2) -0.001487 0.000395 -3.761115
## l(RET_m1, 3) -0.000523 0.000372 -1.408898
## log(TA) -0.000767 0.000412 -1.861296
## CtA 0.001914 0.000753 2.540355
## EBITDAtA 0.004457 0.002761 1.614532
## Leverage 0.000242 0.001432 0.168886
## log(BM) 0.000918 0.000137 6.700045
## DivtoAsset -0.009011 0.017564 -0.513032
## l(log(MarCap), 1) -0.001277 0.000385 -3.319349
## Pr(>|t|)
## fit_AIA:l(ShaRepYes, 1, fill = NA)::0 1.9905e-03 **
## fit_AIA:l(ShaRepYes, 1, fill = NA)::1 4.1370e-02 *
## l(RepIntensity, 1) 6.8320e-09 ***
## l(ShaRepYes, 1, fill = NA)::1 4.8801e-02 *
## ANews 9.4135e-04 ***
## NewsAverage 7.5990e-01
## log(NewsCount) 1.3167e-01
## log(Amihud_monthly) < 2.2e-16 ***
## OMRFlag 7.1080e-08 ***
## l(RET_m1, 1) < 2.2e-16 ***
## l(RET_m1, 2) 1.7713e-04 ***
## l(RET_m1, 3) 1.5912e-01
## log(TA) 6.2941e-02 .
## CtA 1.1196e-02 *
## EBITDAtA 1.0667e-01
## Leverage 8.6591e-01
## log(BM) 3.1657e-11 ***
## DivtoAsset 6.0802e-01
## l(log(MarCap), 1) 9.2875e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005399 Adj. R2: 0.232913
## Within R2: 0.059564
## F-test (1st stage), AIA:l(ShaRepYes, 1, fill = NA)::0: stat = 5,956.3 , p < 2.2e-16, on 2 and 39,349 DoF.
## F-test (1st stage), AIA:l(ShaRepYes, 1, fill = NA)::1: stat = 12,407.4 , p < 2.2e-16, on 2 and 39,349 DoF.
## Wu-Hausman: stat = 1.5813, p = 0.20572, on 2 and 38,119 DoF.
# fitstat(fe_2sls_sharepprevious, ~ wh, cluster = 'Ticker')
fe_2sls_carm0 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + RET_m1_0 + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:RET_m1_0 ~ MarketAIA:RET_m1_0 ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,317 observations removed because of NA and infinite values (RHS: 34,317, IV: 4/4).
summary(fe_2sls_carm0, stage = 2)
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:RET_m1_0, Instr.: MarketAIA:RET_m1_0
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,468
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA:RET_m1_0FALSE -0.000781 0.000254 -3.079899 2.1167e-03 **
## fit_AIA:RET_m1_0TRUE -0.000404 0.000228 -1.774606 7.6211e-02 .
## l(RepIntensity, 1) 0.175884 0.024369 7.217459 9.2533e-13 ***
## RET_m1_0TRUE -0.000614 0.000336 -1.826963 6.7948e-02 .
## ANews -0.000508 0.000156 -3.266247 1.1199e-03 **
## NewsAverage 0.000106 0.000346 0.306268 7.5945e-01
## log(NewsCount) 0.000111 0.000101 1.099756 2.7165e-01
## log(Amihud_monthly) -0.002112 0.000150 -14.046577 < 2.2e-16 ***
## OMRFlag 0.001655 0.000297 5.575364 3.0346e-08 ***
## l(RET_m1, 1) -0.004156 0.000486 -8.555709 < 2.2e-16 ***
## l(RET_m1, 2) -0.001557 0.000397 -3.919960 9.3455e-05 ***
## l(RET_m1, 3) -0.000571 0.000373 -1.530755 1.2609e-01
## log(TA) -0.000569 0.000433 -1.313558 1.8924e-01
## CtA 0.001758 0.000765 2.298310 2.1712e-02 *
## EBITDAtA 0.005379 0.002855 1.884324 5.9757e-02 .
## Leverage -0.000859 0.001512 -0.567879 5.7022e-01
## log(BM) 0.000864 0.000138 6.256478 5.4280e-10 ***
## DivtoAsset -0.008536 0.018372 -0.464632 6.4228e-01
## l(log(MarCap), 1) -0.001505 0.000399 -3.777571 1.6595e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005406 Adj. R2: 0.230789
## Within R2: 0.056959
## F-test (1st stage), AIA:RET_m1_0FALSE: stat = 5,085.3 , p < 2.2e-16 , on 2 and 39,347 DoF.
## F-test (1st stage), AIA:RET_m1_0TRUE : stat = 6,840.4 , p < 2.2e-16 , on 2 and 39,347 DoF.
## Wu-Hausman: stat = 1.61054, p = 0.199793, on 2 and 38,117 DoF.
fe_2sls_carm3 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(l(log(1 + RET_m1),0:-3) > 0) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
AIA:i(l(log(1 + RET_m1),0:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),0:-3) > 0 ),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 52,179 observations removed because of NA and infinite values (RHS: 52,179, IV: 32,057/32,057).
fe_2sls_carm3
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0), Instr.: MarketAIA:i(log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0)
## Second stage: Dep. Var.: RepIntensity
## Observations: 21,606
## Fixed-effects: Ticker: 1,000, time: 66
## Standard-errors: Clustered (Ticker)
## Estimate
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::FALSE -0.000884
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE -0.000471
## l(RepIntensity, 1) 0.163362
## log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE -0.000401
## ANews -0.000259
## NewsAverage 0.000071
## log(NewsCount) -0.000081
## log(Amihud_monthly) -0.002042
## OMRFlag 0.001584
## l(RET_m1, 1) -0.003801
## l(RET_m1, 2) -0.001212
## l(RET_m1, 3) -0.000551
## log(TA) -0.000978
## CtA 0.002033
## EBITDAtA 0.004483
## Leverage 0.001363
## log(BM) 0.000897
## DivtoAsset 0.049638
## l(log(MarCap), 1) -0.000983
## Std. Error
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::FALSE 0.000431
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE 0.000284
## l(RepIntensity, 1) 0.026118
## log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE 0.000539
## ANews 0.000175
## NewsAverage 0.000322
## log(NewsCount) 0.000127
## log(Amihud_monthly) 0.000185
## OMRFlag 0.000378
## l(RET_m1, 1) 0.000552
## l(RET_m1, 2) 0.000515
## l(RET_m1, 3) 0.000435
## log(TA) 0.000458
## CtA 0.000831
## EBITDAtA 0.004034
## Leverage 0.001629
## log(BM) 0.000148
## DivtoAsset 0.043006
## l(log(MarCap), 1) 0.000438
## t value
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::FALSE -2.052763
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE -1.657419
## l(RepIntensity, 1) 6.254819
## log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE -0.744495
## ANews -1.480527
## NewsAverage 0.221083
## log(NewsCount) -0.642588
## log(Amihud_monthly) -11.021083
## OMRFlag 4.191416
## l(RET_m1, 1) -6.891972
## l(RET_m1, 2) -2.353632
## l(RET_m1, 3) -1.267371
## log(TA) -2.137048
## CtA 2.447299
## EBITDAtA 1.111260
## Leverage 0.836809
## log(BM) 6.071381
## DivtoAsset 1.154227
## l(log(MarCap), 1) -2.244551
## Pr(>|t|)
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::FALSE 4.0356e-02
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE 9.7749e-02
## l(RepIntensity, 1) 5.8917e-10
## log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE 4.5675e-01
## ANews 1.3905e-01
## NewsAverage 8.2507e-01
## log(NewsCount) 5.2064e-01
## log(Amihud_monthly) < 2.2e-16
## OMRFlag 3.0178e-05
## l(RET_m1, 1) 9.7331e-12
## l(RET_m1, 2) 1.8784e-02
## l(RET_m1, 3) 2.0532e-01
## log(TA) 3.2836e-02
## CtA 1.4564e-02
## EBITDAtA 2.6672e-01
## Leverage 4.0290e-01
## log(BM) 1.7992e-09
## DivtoAsset 2.4868e-01
## l(log(MarCap), 1) 2.5015e-02
##
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::FALSE *
## fit_AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE .
## l(RepIntensity, 1) ***
## log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE
## ANews
## NewsAverage
## log(NewsCount)
## log(Amihud_monthly) ***
## OMRFlag ***
## l(RET_m1, 1) ***
## l(RET_m1, 2) *
## l(RET_m1, 3)
## log(TA) *
## CtA *
## EBITDAtA
## Leverage
## log(BM) ***
## DivtoAsset
## l(log(MarCap), 1) *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005039 Adj. R2: 0.238934
## Within R2: 0.053414
## F-test (1st stage), AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::FALSE: stat = 2,162.9 , p < 2.2e-16 , on 2 and 21,521 DoF.
## F-test (1st stage), AIA:log(1 + RET_m1) + f(log(1 + RET_m1), 1) + f(log(1 + RET_m1), 2) + f(log(1 + RET_m1), 3) > 0::TRUE : stat = 5,345.2 , p < 2.2e-16 , on 2 and 21,521 DoF.
## Wu-Hausman: stat = 0.350111, p = 0.704614, on 2 and 20,520 DoF.
fe_2sls_pQ <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(l(RET_m1, 1)>0) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
AIA:i(l(RET_m1, 1)>0) ~ MarketAIA:i(l(RET_m1, 1)>0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315, IV: 12,152/12,152).
fe_2sls_pQ
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(l(RET_m1, 1) > 0), Instr.: MarketAIA:i(l(RET_m1, 1) > 0)
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA:l(RET_m1, 1) > 0::FALSE -0.000468 0.000257 -1.821376 6.8793e-02 .
## fit_AIA:l(RET_m1, 1) > 0::TRUE -0.000639 0.000208 -3.072069 2.1726e-03 **
## l(RepIntensity, 1) 0.175878 0.024351 7.222548 8.9271e-13 ***
## l(RET_m1, 1) > 0::TRUE 0.000036 0.000341 0.106678 9.1506e-01
## ANews -0.000520 0.000155 -3.347018 8.4158e-04 ***
## NewsAverage 0.000125 0.000346 0.362007 7.1741e-01
## log(NewsCount) 0.000109 0.000103 1.058184 2.9018e-01
## log(Amihud_monthly) -0.002069 0.000150 -13.775538 < 2.2e-16 ***
## OMRFlag 0.001651 0.000297 5.552919 3.4404e-08 ***
## l(RET_m1, 1) -0.003740 0.000639 -5.851871 6.2287e-09 ***
## l(RET_m1, 2) -0.001547 0.000399 -3.878295 1.1077e-04 ***
## l(RET_m1, 3) -0.000562 0.000371 -1.514857 1.3007e-01
## log(TA) -0.000851 0.000421 -2.021946 4.3398e-02 *
## CtA 0.001768 0.000763 2.317741 2.0627e-02 *
## EBITDAtA 0.004983 0.002835 1.757588 7.9067e-02 .
## Leverage 0.000124 0.001462 0.084876 9.3237e-01
## log(BM) 0.000945 0.000139 6.821462 1.4111e-11 ***
## DivtoAsset -0.008954 0.017970 -0.498287 6.1837e-01
## l(log(MarCap), 1) -0.001175 0.000386 -3.044529 2.3800e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005407 Adj. R2: 0.230476
## Within R2: 0.056576
## F-test (1st stage), AIA:l(RET_m1, 1) > 0::FALSE: stat = 4,905.4 , p < 2.2e-16 , on 2 and 39,349 DoF.
## F-test (1st stage), AIA:l(RET_m1, 1) > 0::TRUE : stat = 7,106.8 , p < 2.2e-16 , on 2 and 39,349 DoF.
## Wu-Hausman: stat = 1.41418, p = 0.243137, on 2 and 38,119 DoF.
#### 7.1 with CAR 1-, 3- and 6-month ======
pdShaRep %>% names
## [1] "YM" "AIAC" "ANews" "NewsAverage"
## [5] "NewsCount" "MarketAIA" "MarketAIAC" "CUSIP"
## [9] "CIK" "gvkey" "Name" "cusip8"
## [13] "CUSIP_tts" "AIA" "ShaRep3" "missing3"
## [17] "Price2" "Amihud_monthly" "OMRFlag" "Ticker"
## [21] "RET_m1" "Volatility" "AvePrice" "TradeVol"
## [25] "TradeVolDollar" "MarCap" "ShaOut" "ShaOutPrevious"
## [29] "datadate" "tic" "cusip" "atq"
## [33] "ceqq" "cheq" "oibdpq" "pstkq"
## [37] "seqq" "txditcq" "cdvcy" "costat"
## [41] "dlcq" "dlttq" "BE" "TD"
## [45] "CtA" "TA" "Year" "Quarter"
## [49] "Month" "RepIntensity" "RepIntensity_vol" "ShaRepYes"
## [53] "BM" "DivtoAsset" "EBITDA" "Leverage"
## [57] "TradeVol_scaled" "EBITDAtA" "time" "sic"
## [61] "shortintadj" "OMR_YM" "ProgramMonth" "ShortInterest"
## [65] "wv_bhar_reb3" "wv_bhar_nreb3" "wv_bhar_reb6" "wv_bhar_nreb6"
## [69] "wv_bhar_reb1" "wv_bhar_nreb1" "wv_bhar_reb0" "wv_bhar_nreb0"
## [73] "TradeVol_shaout" "BM_q1" "BM_q2" "BM_q3"
## [77] "BM_q4" "RET_md" "RET_mean" "frequency"
## [81] "Volatility_md" "UnderPriced" "BM_middle" "PriceQ1"
## [85] "PriceQ2" "PriceQ3" "PriceQ4" "PriceQ"
## [89] "HighVolatility" "RET_m1_0" "YQ"
fe_2sls_CARm3 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(wv_bhar_reb6 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb6 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb6 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
fe_2sls_CARm3
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA:i(wv_bhar_reb6 > 0), Instr.: MarketAIA:i(wv_bhar_reb6 > 0)
## Second stage: Dep. Var.: RepIntensity
## Observations: 33,472
## Fixed-effects: Ticker: 1,115, time: 93
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA:wv_bhar_reb6 > 0::FALSE -0.000883 0.000216 -4.085102 4.7212e-05 ***
## fit_AIA:wv_bhar_reb6 > 0::TRUE -0.000285 0.000205 -1.386244 1.6595e-01
## l(RepIntensity, 1) 0.161063 0.025194 6.392807 2.3905e-10 ***
## wv_bhar_reb6 > 0::TRUE -0.000290 0.000294 -0.988318 3.2321e-01
## ANews -0.000463 0.000170 -2.716348 6.7028e-03 **
## NewsAverage 0.000083 0.000382 0.218290 8.2724e-01
## log(NewsCount) 0.000053 0.000106 0.494705 6.2091e-01
## log(Amihud_monthly) -0.002037 0.000161 -12.652363 < 2.2e-16 ***
## OMRFlag 0.001631 0.000337 4.834446 1.5222e-06 ***
## l(RET_m1, 1) -0.004136 0.000501 -8.248340 4.4888e-16 ***
## l(RET_m1, 2) -0.001488 0.000426 -3.494269 4.9404e-04 ***
## l(RET_m1, 3) -0.000805 0.000435 -1.850430 6.4516e-02 .
## log(TA) -0.001104 0.000451 -2.445751 1.4609e-02 *
## CtA 0.002000 0.000818 2.444367 1.4665e-02 *
## EBITDAtA 0.002166 0.002721 0.795720 4.2636e-01
## Leverage 0.000604 0.001552 0.389121 6.9726e-01
## log(BM) 0.001097 0.000171 6.414184 2.0884e-10 ***
## DivtoAsset -0.037533 0.024267 -1.546656 1.2223e-01
## l(log(MarCap), 1) -0.000869 0.000423 -2.054018 4.0207e-02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.0053 Adj. R2: 0.225262
## Within R2: 0.050838
## F-test (1st stage), AIA:wv_bhar_reb6 > 0::FALSE: stat = 4,985.5 , p < 2.2e-16, on 2 and 33,360 DoF.
## F-test (1st stage), AIA:wv_bhar_reb6 > 0::TRUE : stat = 6,194.8 , p < 2.2e-16, on 2 and 33,360 DoF.
## Wu-Hausman: stat = 2.72349, p = 0.06566, on 2 and 32,244 DoF.
#### 7.11 Wald test.
# aod::wald.test(Sigma = fixest::vcov_cluster(fe_2sls_CARm0, cluster = c("Ticker")), b = fe_2sls_CARm3$coefficients, Terms = 1:2)
{
fe_2sls_CARm0 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(wv_bhar_reb0 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb0 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb0 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
aod::wald.test(Sigma = fixest::vcov_cluster(fe_2sls_CARm0, cluster = c("Ticker")), b = fe_2sls_CARm3$coefficients, Terms = 1:2)
fe_2sls_CARm1 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(wv_bhar_reb1 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb1 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb1 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
aod::wald.test(Sigma = fixest::vcov_cluster(fe_2sls_CARm1, cluster = c("Ticker")), b = fe_2sls_CARm3$coefficients, Terms = 1:2)
fe_2sls_CARm3 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(wv_bhar_reb3 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb3 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb3 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
aod::wald.test(Sigma = fixest::vcov_cluster(fe_2sls_CARm3, cluster = c("Ticker")), b = fe_2sls_CARm3$coefficients, Terms = 1:2)
fe_2sls_CARm6 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(wv_bhar_reb6 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb6 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb6 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
aod::wald.test(Sigma = fixest::vcov_cluster(fe_2sls_CARm6, cluster = c("Ticker")), b = fe_2sls_CARm3$coefficients, Terms = 1:2)
etable(summary(fe_2sls_CARm0, stage = 2),
summary(fe_2sls_CARm1, stage = 2),
summary(fe_2sls_CARm3, stage = 2),
summary(fe_2sls_CARm6, stage = 2),
fitstat = ~ . + ivfall + ivwaldall + wh + wh.p,
tex = T)
}
## Warning in log(BM): NaNs produced
## NOTE: 40,362 observations removed because of NA and infinite values (RHS: 40,362, IV: 14,644/14,644).
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
## \begingroup
## \centering
## \begin{tabular}{lcccc}
## \tabularnewline \midrule \midrule
## Dependent Variable: & \multicolumn{4}{c}{RepIntensity}\\
## Model: & (1) & (2) & (3) & (4)\\
## \midrule
## \emph{Variables}\\
## AIA:wv\_bhar\_reb0>0::FALSE & -0.0009$^{***}$ & & & \\
## & (0.0002) & & & \\
## AIA:wv\_bhar\_reb0>0::TRUE & -0.0002 & & & \\
## & (0.0002) & & & \\
## l(RepIntensity,1) & 0.1611$^{***}$ & 0.1611$^{***}$ & 0.1609$^{***}$ & 0.1611$^{***}$\\
## & (0.0252) & (0.0252) & (0.0252) & (0.0252)\\
## wv\_bhar\_reb0>0 $=$ TRUE & -0.0007$^{**}$ & & & \\
## & (0.0003) & & & \\
## ANews & -0.0005$^{***}$ & -0.0005$^{***}$ & -0.0005$^{***}$ & -0.0005$^{***}$\\
## & (0.0002) & (0.0002) & (0.0002) & (0.0002)\\
## NewsAverage & $8.56\times 10^{-5}$ & 0.0001 & $7.48\times 10^{-5}$ & $8.34\times 10^{-5}$\\
## & (0.0004) & (0.0004) & (0.0004) & (0.0004)\\
## log(NewsCount) & $5.26\times 10^{-5}$ & $4.57\times 10^{-5}$ & $5.45\times 10^{-5}$ & $5.26\times 10^{-5}$\\
## & (0.0001) & (0.0001) & (0.0001) & (0.0001)\\
## log(Amihud\_monthly) & -0.0021$^{***}$ & -0.0021$^{***}$ & -0.0021$^{***}$ & -0.0020$^{***}$\\
## & (0.0002) & (0.0002) & (0.0002) & (0.0002)\\
## OMRFlag & 0.0016$^{***}$ & 0.0016$^{***}$ & 0.0016$^{***}$ & 0.0016$^{***}$\\
## & (0.0003) & (0.0003) & (0.0003) & (0.0003)\\
## l(RET\_m1,1) & -0.0043$^{***}$ & -0.0041$^{***}$ & -0.0041$^{***}$ & -0.0041$^{***}$\\
## & (0.0005) & (0.0005) & (0.0005) & (0.0005)\\
## l(RET\_m1,2) & -0.0015$^{***}$ & -0.0015$^{***}$ & -0.0015$^{***}$ & -0.0015$^{***}$\\
## & (0.0004) & (0.0004) & (0.0004) & (0.0004)\\
## l(RET\_m1,3) & -0.0008$^{*}$ & -0.0008$^{*}$ & -0.0008$^{*}$ & -0.0008$^{*}$\\
## & (0.0004) & (0.0004) & (0.0004) & (0.0004)\\
## log(TA) & -0.0009$^{*}$ & -0.0011$^{**}$ & -0.0011$^{**}$ & -0.0011$^{**}$\\
## & (0.0005) & (0.0005) & (0.0005) & (0.0005)\\
## CtA & 0.0020$^{**}$ & 0.0020$^{**}$ & 0.0020$^{**}$ & 0.0020$^{**}$\\
## & (0.0008) & (0.0008) & (0.0008) & (0.0008)\\
## EBITDAtA & 0.0024 & 0.0021 & 0.0022 & 0.0022\\
## & (0.0027) & (0.0027) & (0.0027) & (0.0027)\\
## Leverage & $-9.5\times 10^{-5}$ & 0.0008 & 0.0008 & 0.0006\\
## & (0.0016) & (0.0016) & (0.0016) & (0.0016)\\
## log(BM) & 0.0010$^{***}$ & 0.0011$^{***}$ & 0.0011$^{***}$ & 0.0011$^{***}$\\
## & (0.0002) & (0.0002) & (0.0002) & (0.0002)\\
## DivtoAsset & -0.0394 & -0.0388 & -0.0389$^{*}$ & -0.0375\\
## & (0.0244) & (0.0242) & (0.0235) & (0.0243)\\
## l(log(MarCap),1) & -0.0012$^{***}$ & -0.0009$^{**}$ & -0.0009$^{**}$ & -0.0009$^{**}$\\
## & (0.0004) & (0.0004) & (0.0004) & (0.0004)\\
## AIA:wv\_bhar\_reb1>0::FALSE & & -0.0007$^{***}$ & & \\
## & & (0.0002) & & \\
## AIA:wv\_bhar\_reb1>0::TRUE & & -0.0005$^{**}$ & & \\
## & & (0.0002) & & \\
## wv\_bhar\_reb1>0 $=$ TRUE & & $-9.11\times 10^{-5}$ & & \\
## & & (0.0003) & & \\
## AIA:wv\_bhar\_reb3>0::FALSE & & & -0.0009$^{***}$ & \\
## & & & (0.0002) & \\
## AIA:wv\_bhar\_reb3>0::TRUE & & & -0.0003 & \\
## & & & (0.0002) & \\
## wv\_bhar\_reb3>0 $=$ TRUE & & & -0.0003 & \\
## & & & (0.0003) & \\
## AIA:wv\_bhar\_reb6>0::FALSE & & & & -0.0009$^{***}$\\
## & & & & (0.0002)\\
## AIA:wv\_bhar\_reb6>0::TRUE & & & & -0.0003\\
## & & & & (0.0002)\\
## wv\_bhar\_reb6>0 $=$ TRUE & & & & -0.0003\\
## & & & & (0.0003)\\
## \midrule
## \emph{Fixed-effects}\\
## Ticker & Yes & Yes & Yes & Yes\\
## time & Yes & Yes & Yes & Yes\\
## \midrule
## \emph{Fit statistics}\\
## Observations & 33,423 & 33,472 & 33,472 & 33,472\\
## R$^2$ & 0.25368 & 0.25507 & 0.25370 & 0.25362\\
## Within R$^2$ & 0.05106 & 0.05269 & 0.05095 & 0.05084\\
## F-test (IV only) & 24.002 & 21.895 & 23.341 & 23.192\\
## Wald (IV only) & 14.504 & 13.247 & 13.842 & 14.790\\
## Wu-Hausman & 2.4000 & 0.56867 & 2.5708 & 2.7235\\
## Wu-Hausman, p-value & 0.09074 & 0.56628 & 0.07649 & 0.06566\\
## \midrule \midrule
## \multicolumn{5}{l}{\emph{Clustered (Ticker) standard-errors in parentheses}}\\
## \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.01, **: 0.05, *: 0.1}}\\
## \end{tabular}
## \par\endgroup
if (0 == 1) {
fe_2sls_CARm3_extra <- feols(fml = RepIntensity ~ l(RepIntensity, 1) + i(group6) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(group6) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(group6), # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
fe_2sls_CARm3_extra
summary(fe_2sls_CARm3_extra, stage = 2)
ggplot(data = data.frame(pdShaRep)) +
geom_histogram(aes(x = group1)) +
geom_histogram(aes(x = group3)) +
geom_histogram(aes(x = group6))
}
feols(fml = RepIntensity ~ AIA:i(l(ShaRepYes, 1, fill = NA)) + l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time, # | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# ~ MarketAIA:l(1-ShaRepYes, 1, fill = 0) + MarketAIA:l(ShaRepYes, 1, fill = 0) ,
# AIA:i(l(RET_m1,-1) > 0) ~ MarketAIA:i(l(RET_m1,-1) > 0),
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker")) # %>%
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## l(RepIntensity, 1) 0.164397 0.025268 6.506190 1.1197e-10 ***
## ANews -0.000513 0.000156 -3.293381 1.0181e-03 **
## NewsAverage 0.000042 0.000348 0.119814 9.0465e-01
## log(NewsCount) 0.000130 0.000101 1.284798 1.9911e-01
## log(Amihud_monthly) -0.002084 0.000149 -13.955516 < 2.2e-16 ***
## OMRFlag 0.001617 0.000296 5.457706 5.8301e-08 ***
## l(RET_m1, 1) -0.003997 0.000475 -8.415691 < 2.2e-16 ***
## l(RET_m1, 2) -0.001542 0.000397 -3.887928 1.0651e-04 ***
## ... 10 coefficients remaining (display them with summary() or use argument n)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005402 Adj. R2: 0.232004
## Within R2: 0.058425
# su <- ary()
{
fe_2sls_prob_CARm0 <- feols(fml = ShaRepYes ~ l(ShaRepYes, 1) + # wv_bhar_reb0 + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA*i(wv_bhar_reb3>0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA*i(wv_bhar_reb3>0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
fe_2sls_prob_CARm1 <- feols(fml = ShaRepYes ~ l(ShaRepYes, 1) + i(wv_bhar_reb1 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb1 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb1 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
fe_2sls_prob_CARm3 <- feols(fml = ShaRepYes ~ l(ShaRepYes, 1) + i(wv_bhar_reb3 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb3 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb3 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
fe_2sls_prob_CARm6 <- feols(fml = ShaRepYes ~ l(ShaRepYes, 1) + i(wv_bhar_reb6 > 0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA:i(wv_bhar_reb6 > 0) ~ # i((wv_bhar_reb6 > 0) + RET_m1_0) ~
MarketAIA:i(wv_bhar_reb6 > 0) , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
feols(fml = ShaRepYes ~ l(ShaRepYes, 1) + #i(wv_bhar_reb1>0) + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | # AIA ~ MarketAIA,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM)
# AIA:(1-UnderPriced) + AIA:UnderPriced ~ MarketAIA:(1-UnderPriced) + MarketAIA:UnderPriced, ## UnderPriced versus Overpriced
# AIA:l(1-ShaRepYes, 1, fill = NA) + AIA:l(ShaRepYes, 1, fill = NA) ~ MarketAIA:l(1-ShaRepYes, 1, fill = NA) + MarketAIA:l(ShaRepYes, 1, fill = NA) ,
AIA ~ MarketAIA , # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep[pdShaRep$wv_bhar_reb3 <= 0,], #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
etable(summary(fe_2sls_prob_CARm0, stage = 2),
summary(fe_2sls_prob_CARm1, stage = 2),
summary(fe_2sls_prob_CARm3, stage = 2),
summary(fe_2sls_prob_CARm6, stage = 2),
fitstat = ~ . + ivfall + ivwaldall + wh + wh.p,
tex = F)
}
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 34,315, IV: 14,523/14,523).
## The endogenous regressor 'fit_AIA:wv_bhar_reb3 > 0::TRUE' have been removed because of collinearity (see $collin.var).
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (RHS: 40,313, IV: 14,523/14,523).
## Warning in log(BM): NaNs produced
## NOTE: 37,518 observations removed because of NA and infinite values (LHS: 14,523, RHS: 37,518, IV: 14,523/14,523, Fixed-effects: 14,523).
## summary(fe_2sls_p.. summary(fe_2sls_p...1
## Dependent Var.: ShaRepYes ShaRepYes
##
## AIA -0.0213 (0.0169)
## wv_bhar_reb3>0::TRUE -0.0072 (0.0226)
## AIA:wv_bhar_reb3>0::FALSE -0.0212 (0.0288)
## l(ShaRepYes,1) 0.3558*** (0.0136) 0.3558*** (0.0134)
## ANews -0.0278* (0.0108) -0.0278** (0.0099)
## NewsAverage 0.0527* (0.0226) 0.0539** (0.0189)
## log(NewsCount) -0.0069 (0.0071) -0.0069 (0.0068)
## log(Amihud_monthly) -0.0612*** (0.0093) -0.0611*** (0.0090)
## OMRFlag 0.0784*** (0.0167) 0.0784*** (0.0159)
## l(RET_m1,1) -0.1457*** (0.0285) -0.1457*** (0.0273)
## l(RET_m1,2) -0.0439. (0.0253) -0.0441. (0.0236)
## l(RET_m1,3) -0.0283 (0.0253) -0.0277 (0.0242)
## log(TA) -0.1279*** (0.0362) -0.1277*** (0.0345)
## CtA -0.0081 (0.0625) -0.0084 (0.0583)
## EBITDAtA 0.1919 (0.2569) 0.1906 (0.2467)
## Leverage 0.0126 (0.1121) 0.0122 (0.1062)
## log(BM) 0.0515*** (0.0123) 0.0514*** (0.0115)
## DivtoAsset -0.3449 (1.752) -0.3827 (1.765)
## l(log(MarCap),1) 0.0618* (0.0296) 0.0604* (0.0284)
## AIA:wv_bhar_reb1>0::FALSE -0.0505*** (0.0149)
## AIA:wv_bhar_reb1>0::TRUE -0.0145 (0.0146)
## wv_bhar_reb1>0 = TRUE -0.0204 (0.0202)
## AIA:wv_bhar_reb3>0::TRUE
## wv_bhar_reb3>0 = TRUE
## AIA:wv_bhar_reb6>0::FALSE
## AIA:wv_bhar_reb6>0::TRUE
## wv_bhar_reb6>0 = TRUE
## Fixed-Effects: ------------------- -------------------
## Ticker Yes Yes
## time Yes Yes
## _________________________ ___________________ ___________________
## S.E.: Clustered by: Ticker by: Ticker
## Observations 33,472 33,472
## R2 0.44674 0.50846
## Within R2 0.03160 0.13963
## F-test (IV only) 12.483 16.769
## Wald (IV only) 4.4104 9.9371
## Wu-Hausman -- 0.87668
## Wu-Hausman, p-value -- 0.41617
##
## summary(fe_2sls_p...2 summary(fe_2sls_p...3
## Dependent Var.: ShaRepYes ShaRepYes
##
## AIA
## wv_bhar_reb3>0::TRUE
## AIA:wv_bhar_reb3>0::FALSE -0.0425** (0.0142)
## l(ShaRepYes,1) 0.3558*** (0.0134) 0.3559*** (0.0134)
## ANews -0.0278** (0.0100) -0.0278** (0.0100)
## NewsAverage 0.0527** (0.0190) 0.0535** (0.0190)
## log(NewsCount) -0.0069 (0.0068) -0.0071 (0.0068)
## log(Amihud_monthly) -0.0612*** (0.0090) -0.0612*** (0.0091)
## OMRFlag 0.0784*** (0.0159) 0.0782*** (0.0159)
## l(RET_m1,1) -0.1457*** (0.0272) -0.1462*** (0.0272)
## l(RET_m1,2) -0.0439. (0.0236) -0.0436. (0.0236)
## l(RET_m1,3) -0.0283 (0.0242) -0.0282 (0.0242)
## log(TA) -0.1279*** (0.0346) -0.1272*** (0.0345)
## CtA -0.0081 (0.0584) -0.0084 (0.0583)
## EBITDAtA 0.1919 (0.2470) 0.1883 (0.2455)
## Leverage 0.0126 (0.1063) 0.0065 (0.1058)
## log(BM) 0.0515*** (0.0116) 0.0511*** (0.0116)
## DivtoAsset -0.3449 (1.733) -0.3638 (1.768)
## l(log(MarCap),1) 0.0618* (0.0285) 0.0622* (0.0286)
## AIA:wv_bhar_reb1>0::FALSE
## AIA:wv_bhar_reb1>0::TRUE
## wv_bhar_reb1>0 = TRUE
## AIA:wv_bhar_reb3>0::TRUE -0.0213 (0.0141)
## wv_bhar_reb3>0 = TRUE -0.0072 (0.0192)
## AIA:wv_bhar_reb6>0::FALSE -0.0352* (0.0158)
## AIA:wv_bhar_reb6>0::TRUE -0.0283* (0.0142)
## wv_bhar_reb6>0 = TRUE 0.0065 (0.0209)
## Fixed-Effects: ------------------- -------------------
## Ticker Yes Yes
## time Yes Yes
## _________________________ ___________________ ___________________
## S.E.: Clustered by: Ticker by: Ticker
## Observations 33,472 33,472
## R2 0.50875 0.50887
## Within R2 0.14015 0.14035
## F-test (IV only) 15.971 15.729
## Wald (IV only) 9.0067 8.4923
## Wu-Hausman 0.35721 0.35454
## Wu-Hausman, p-value 0.69963 0.70150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### 8. falsification test for the exclusive restriction ----
### 8.1 use the last AIA
fe_2sls_exclusiveres <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | l(AIA,1) ~ l(MarketAIA,1), # + Ticker^yearqtr(YM) # + Ticker^YQ
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315, IV: 12,149/12,149).
summary(fe_2sls_exclusiveres, stage = 1:2)
## IV: First stage: l(AIA, 1)
## TSLS estimation, Dep. Var.: l(AIA, 1), Endo.: l(AIA, 1), Instr.: l(MarketAIA, 1)
## First stage: Dep. Var.: l(AIA, 1)
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## l(MarketAIA, 1) -1257.454989 4.589444 -273.988495 < 2.2e-16 ***
## l(RepIntensity, 1) -0.203196 0.094104 -2.159273 0.031022 *
## ANews 0.005331 0.003466 1.538051 0.124294
## NewsAverage -0.015143 0.006328 -2.393021 0.016860 *
## log(NewsCount) 0.005352 0.002305 2.322528 0.020367 *
## log(Amihud_monthly) -0.007360 0.003488 -2.109889 0.035070 *
## OMRFlag 0.000454 0.002938 0.154369 0.877344
## l(RET_m1, 1) -0.006768 0.008759 -0.772685 0.439858
## l(RET_m1, 2) -0.011056 0.007643 -1.446601 0.148264
## l(RET_m1, 3) 0.000686 0.008374 0.081932 0.934714
## log(TA) 0.005039 0.010472 0.481180 0.630474
## CtA -0.015659 0.018446 -0.848878 0.396115
## EBITDAtA 0.094871 0.037581 2.524409 0.011715 *
## Leverage -0.042912 0.035337 -1.214348 0.224849
## log(BM) -0.001865 0.002661 -0.701040 0.483411
## DivtoAsset -0.323973 0.495359 -0.654015 0.513224
## l(log(MarCap), 1) -0.012468 0.008544 -1.459237 0.144756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.141173 Adj. R2: 0.958238
## Within R2: 0.87927
## F-test (1st stage): stat = 273,150.5, p < 2.2e-16, on 1 and 39,351 DoF.
##
## IV: Second stage
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: l(AIA, 1), Instr.: l(MarketAIA, 1)
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_l(AIA, 1) 0.000108 0.000098 1.108937 2.6767e-01
## l(RepIntensity, 1) 0.176311 0.024416 7.221222 9.0110e-13 ***
## ANews -0.000552 0.000157 -3.523424 4.4166e-04 ***
## NewsAverage -0.000125 0.000353 -0.352920 7.2421e-01
## log(NewsCount) 0.000027 0.000100 0.265088 7.9099e-01
## log(Amihud_monthly) -0.002050 0.000150 -13.663296 < 2.2e-16 ***
## OMRFlag 0.001637 0.000297 5.509241 4.3866e-08 ***
## l(RET_m1, 1) -0.003929 0.000475 -8.264944 3.5887e-16 ***
## l(RET_m1, 2) -0.001536 0.000397 -3.863908 1.1742e-04 ***
## l(RET_m1, 3) -0.000546 0.000373 -1.463218 1.4366e-01
## log(TA) -0.000858 0.000424 -2.024575 4.3127e-02 *
## CtA 0.001674 0.000755 2.216749 2.6823e-02 *
## EBITDAtA 0.005156 0.002813 1.832958 6.7051e-02 .
## Leverage -0.000084 0.001466 -0.057275 9.5434e-01
## log(BM) 0.000937 0.000139 6.727464 2.6407e-11 ***
## DivtoAsset -0.009019 0.017976 -0.501724 6.1595e-01
## l(log(MarCap), 1) -0.001235 0.000389 -3.174696 1.5372e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.00541 Adj. R2: 0.229841
## Within R2: 0.055749
## F-test (1st stage), l(AIA, 1): stat = 273,150.5 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 1.38e-4, p = 0.990627, on 1 and 38,122 DoF.
feols(fml = ANews ~ AIA + MarketAIA
| Ticker + time, # + Ticker^yearqtr(YM) # + Ticker^YQ
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## NOTE: 740 observations removed because of NA values (LHS: 740).
## OLS estimation, Dep. Var.: ANews
## Observations: 73,045
## Fixed-effects: Ticker: 1,567, time: 141
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## AIA 0.226433 0.007620 29.71437 < 2.2e-16 ***
## MarketAIA 0.069528 0.019398 3.58427 0.00034839 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.347078 Adj. R2: 0.637503
## Within R2: 0.070237
### 8.2 should not predict the dividend-to-asset ratio
feols(fml = DivtoAsset ~ l(RepIntensity, 0:1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (LHS: 540, RHS: 34,315).
## TSLS estimation, Dep. Var.: DivtoAsset, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: DivtoAsset
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA 0.000000181 0.00001176 0.015395 0.9877194
## RepIntensity -0.000139770 0.00022406 -0.623814 0.5328656
## l(RepIntensity, 1) 0.000088773 0.00038300 0.231784 0.8167442
## ANews -0.000018381 0.00001598 -1.150111 0.2503221
## NewsAverage -0.000003287 0.00002794 -0.117648 0.9063658
## log(NewsCount) 0.000016542 0.00001257 1.315658 0.1885343
## log(Amihud_monthly) 0.000008983 0.00001093 0.822014 0.4112286
## l(RET_m1, 1) -0.000071896 0.00002216 -3.243902 0.0012108 **
## l(RET_m1, 2) -0.000038052 0.00011632 -0.327127 0.7436275
## l(RET_m1, 3) 0.000030429 0.00003236 0.940289 0.3472541
## log(TA) 0.000001502 0.00001894 0.079328 0.9367846
## CtA -0.000021272 0.00002613 -0.814199 0.4156888
## EBITDAtA -0.000258891 0.00025422 -1.018355 0.3087100
## Leverage -0.000206794 0.00009569 -2.161092 0.0308811 *
## log(BM) -0.000008515 0.00000580 -1.468294 0.1422804
## l(log(MarCap), 1) -0.000016113 0.00001818 -0.886400 0.3755754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 6.227e-4 Adj. R2: 0.871753
## Within R2: 5.177e-4
## F-test (1st stage), AIA: stat = 211,645.5 , p < 2.2e-16 , on 1 and 39,352 DoF.
## Wu-Hausman: stat = 0.434154, p = 0.509962, on 1 and 38,123 DoF.
feols(fml = Leverage ~ l(RepIntensity, 0:1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (DivtoAsset) + (EBITDAtA) + log(BM) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time |
AIA:RET_m1_0 ~ MarketAIA:RET_m1_0 ,
# AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,317 observations removed because of NA and infinite values (LHS: 540, RHS: 34,315, IV: 4/4).
## TSLS estimation, Dep. Var.: Leverage, Endo.: AIA:RET_m1_0, Instr.: MarketAIA:RET_m1_0
## Second stage: Dep. Var.: Leverage
## Observations: 39,468
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA:RET_m1_0FALSE 0.010146 0.001112 9.121017 < 2.2e-16 ***
## fit_AIA:RET_m1_0TRUE -0.004885 0.001074 -4.547583 5.9632e-06 ***
## RepIntensity -0.023163 0.043302 -0.534917 5.9280e-01
## l(RepIntensity, 1) -0.044052 0.040563 -1.085994 2.7769e-01
## ANews 0.000292 0.001608 0.181319 8.5615e-01
## NewsAverage -0.002562 0.002991 -0.856658 3.9180e-01
## log(NewsCount) 0.001619 0.000876 1.847768 6.4876e-02 .
## log(Amihud_monthly) 0.003259 0.001475 2.210375 2.7263e-02 *
## l(RET_m1, 1) -0.073699 0.004463 -16.513230 < 2.2e-16 ***
## l(RET_m1, 2) 0.010782 0.002634 4.093619 4.5244e-05 ***
## l(RET_m1, 3) 0.011703 0.002615 4.474748 8.3596e-06 ***
## log(TA) 0.233877 0.006558 35.660906 < 2.2e-16 ***
## CtA -0.042188 0.013210 -3.193732 1.4401e-03 **
## DivtoAsset -0.441727 0.255985 -1.725594 8.4672e-02 .
## EBITDAtA 0.002433 0.058370 0.041681 9.6676e-01
## log(BM) -0.042088 0.003329 -12.641748 < 2.2e-16 ***
## l(log(MarCap), 1) -0.226522 0.004871 -46.505881 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.029585 Adj. R2: 0.984708
## Within R2: 0.805753
## F-test (1st stage), AIA:RET_m1_0FALSE: stat = 25,033.3, p < 2.2e-16, on 2 and 39,349 DoF.
## F-test (1st stage), AIA:RET_m1_0TRUE : stat = 25,179.8, p < 2.2e-16, on 2 and 39,349 DoF.
## Wu-Hausman: stat = 346.9, p < 2.2e-16, on 2 and 38,119 DoF.
#### 9. results on OMR program announcement ----
OMR_2sls_exclusiveres <- feols(fml = OMRFlag ~ l(RepIntensity, 0:1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 34,315 observations removed because of NA and infinite values (RHS: 34,315).
summary(OMR_2sls_exclusiveres, stage = 1:2)
## IV: First stage: AIA
## TSLS estimation, Dep. Var.: AIA, Endo.: AIA, Instr.: MarketAIA
## First stage: Dep. Var.: AIA
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## MarketAIA -1246.070090 5.221752 -238.630667 < 2.2e-16 ***
## RepIntensity -0.201093 0.099950 -2.011935 4.4445e-02 *
## l(RepIntensity, 1) -0.007104 0.103575 -0.068584 9.4533e-01
## ANews 0.012113 0.004060 2.983772 2.9034e-03 **
## NewsAverage -0.021611 0.008654 -2.497141 1.2650e-02 *
## log(NewsCount) 0.017206 0.002218 7.758471 1.7983e-14 ***
## log(Amihud_monthly) -0.006646 0.003430 -1.937403 5.2925e-02 .
## l(RET_m1, 1) -0.014759 0.008533 -1.729557 8.3961e-02 .
## l(RET_m1, 2) -0.006034 0.008688 -0.694598 4.8744e-01
## l(RET_m1, 3) 0.002680 0.011849 0.226211 8.2107e-01
## log(TA) 0.002461 0.011163 0.220501 8.2552e-01
## CtA -0.019795 0.017661 -1.120828 2.6258e-01
## EBITDAtA 0.045280 0.040768 1.110677 2.6692e-01
## Leverage -0.028380 0.041235 -0.688261 4.9142e-01
## log(BM) -0.000781 0.002782 -0.280666 7.7901e-01
## DivtoAsset -0.853511 0.631837 -1.350841 1.7700e-01
## l(log(MarCap), 1) -0.005787 0.008917 -0.649011 5.1645e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.146316 Adj. R2: 0.954453
## Within R2: 0.866116
## F-test (1st stage): stat = 211,643.0, p < 2.2e-16, on 1 and 39,351 DoF.
##
## IV: Second stage
## TSLS estimation, Dep. Var.: OMRFlag, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: OMRFlag
## Observations: 39,470
## Fixed-effects: Ticker: 1,229, time: 102
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA 0.006386 0.002222 2.874523 4.1161e-03 **
## RepIntensity 1.190409 0.228329 5.213571 2.1717e-07 ***
## l(RepIntensity, 1) 0.516513 0.178597 2.892051 3.8948e-03 **
## ANews -0.007943 0.004058 -1.957344 5.0533e-02 .
## NewsAverage 0.027877 0.011002 2.533801 1.1407e-02 *
## log(NewsCount) 0.020554 0.002443 8.412411 < 2.2e-16 ***
## log(Amihud_monthly) 0.002971 0.002605 1.140479 2.5431e-01
## l(RET_m1, 1) -0.022979 0.008462 -2.715442 6.7116e-03 **
## l(RET_m1, 2) -0.008146 0.008020 -1.015689 3.0998e-01
## l(RET_m1, 3) -0.017293 0.008840 -1.956271 5.0660e-02 .
## log(TA) 0.015648 0.007430 2.105963 3.5410e-02 *
## CtA 0.026385 0.014704 1.794450 7.2987e-02 .
## EBITDAtA 0.151610 0.051862 2.923331 3.5266e-03 **
## Leverage -0.086045 0.025197 -3.414867 6.5896e-04 ***
## log(BM) 0.000598 0.002761 0.216505 8.2863e-01
## DivtoAsset -1.046436 0.568161 -1.841795 6.5746e-02 .
## l(log(MarCap), 1) -0.012352 0.007081 -1.744254 8.1365e-02 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.144981 Adj. R2: 0.033846
## Within R2: 0.010732
## F-test (1st stage), AIA: stat = 211,643.0 , p < 2.2e-16 , on 1 and 39,351 DoF.
## Wu-Hausman: stat = 4.09995, p = 0.042891, on 1 and 38,122 DoF.
feols(fml = OMRFlag ~ l(AIA, 1:3), data = pdShaRep)
## NOTE: 32,051 observations removed because of NA values (RHS: 32,051).
## OLS estimation, Dep. Var.: OMRFlag
## Observations: 41,734
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.021270 0.001632 13.03699 < 2.2e-16 ***
## l(AIA, 1) 0.003868 0.001781 2.17145 0.0300825 *
## l(AIA, 2) -0.005682 0.001802 -3.15277 0.0016552 **
## l(AIA, 3) 0.004359 0.001922 2.26781 0.0235086 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.150627 Adj. R2: 5.376e-4
#### 10. future performance for firms making more repurchases ----
feols(fml = wv_bhar_reb1 ~ RepIntensity + # i((wv_bhar_reb6 > 0) + RET_m1_0) + # i(l(RET_m1 > RET_mean,-1)) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + l(OMRFlag, 0, fill = 0) + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) +
l(log(MarCap), 1) # + d(Volatility,1) + l(Volatility, 1) + (TradeVol_scaled) # + lambda
| Ticker + time | AIA ~ MarketAIA, # i((wv_bhar_reb6 > 0) + RET_m1_0) ,
# AIA:i(l(log(1 + RET_m1),-1:-3) > 0) ~ MarketAIA:i(l(log(1 + RET_m1),-1:-3) > 0),
data = pdShaRep, #[pdShaRep$ShaRepYes == T,],
# data = pdShaRep[pdShaRep$RepIntensity > 0,],
cluster = c("Ticker"))
## Warning in log(BM): NaNs produced
## NOTE: 40,313 observations removed because of NA and infinite values (LHS: 14,523, RHS: 34,315).
## TSLS estimation, Dep. Var.: wv_bhar_reb1, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: wv_bhar_reb1
## Observations: 33,472
## Fixed-effects: Ticker: 1,115, time: 93
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.017290 0.127053 -0.136083 8.9178e-01
## RepIntensity 7.178013 9.404712 0.763236 4.4548e-01
## ANews 0.196277 0.201184 0.975609 3.2947e-01
## NewsAverage 0.253320 0.353091 0.717437 4.7325e-01
## log(NewsCount) -0.243883 0.130363 -1.870797 6.1635e-02 .
## log(Amihud_monthly) 0.542075 0.178541 3.036142 2.4519e-03 **
## OMRFlag -0.038271 0.217472 -0.175980 8.6034e-01
## l(RET_m1, 1) -0.819595 1.670687 -0.490574 6.2382e-01
## l(RET_m1, 2) 1.519943 0.862800 1.761640 7.8404e-02 .
## l(RET_m1, 3) -0.268396 0.690309 -0.388806 6.9749e-01
## log(TA) -1.533991 0.630484 -2.433036 1.5129e-02 *
## CtA 1.048243 1.017222 1.030496 3.0300e-01
## EBITDAtA 7.047816 4.896683 1.439304 1.5035e-01
## Leverage 8.796162 2.011370 4.373220 1.3389e-05 ***
## log(BM) 0.394755 0.231047 1.708551 8.7813e-02 .
## DivtoAsset -149.025900 51.986652 -2.866619 4.2269e-03 **
## l(log(MarCap), 1) -0.361545 0.629519 -0.574319 5.6587e-01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 7.53859 Adj. R2: 0.062574
## Within R2: 0.015179
## F-test (1st stage), AIA: stat = 205,251.5 , p < 2.2e-16 , on 1 and 33,362 DoF.
## Wu-Hausman: stat = 1.24665, p = 0.264201, on 1 and 32,247 DoF.
feols(fml = wv_bhar_reb6 ~ ShaRepYes | Ticker + time, data = pdShaRep)
## NOTE: 14,523 observations removed because of NA values (LHS: 14,523).
## OLS estimation, Dep. Var.: wv_bhar_reb6
## Observations: 59,262
## Fixed-effects: Ticker: 1,440, time: 129
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## ShaRepYes 0.173027 0.35409 0.488653 0.62516
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 21.5 Adj. R2: 0.096536
## Within R2: 9.69e-6
#### 11.
{
fe_2sls_robust1 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time | AIA ~ MarketAIA, # + Ticker^yearqtr(YM) # + Ticker^YQ # + sic*time
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker", "time"))
# summary(fe_2sls_robust1, stage = 1:2)
fe_2sls_robust2 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time + Ticker^year(YM)| AIA ~ MarketAIA, # # + Ticker^YQ # + sic*time
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker", "time"))
fe_2sls_robust3 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time + sic*time| AIA ~ MarketAIA, # # + Ticker^YQ #
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker", "time"))
fe_2sls_robust4 <- feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time + Ticker^yearqtr(YM)| AIA ~ MarketAIA, # # + Ticker^YQ #
data = pdShaRep, # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker", "time")) # "Ticker^time"
}
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679).
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679).
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679, Fixed-effects: 1,243).
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679).
## The exogenous variable 'DivtoAsset' have been removed because of collinearity (see $collin.var).
etable(summary(fe_2sls, stage = 2),
summary(fe_2sls_robust1, stage = 2),
summary(fe_2sls_robust2, stage = 2), # firm*year FE
summary(fe_2sls_robust3, stage = 2), # industry*time FE
summary(fe_2sls_robust4, stage = 2), # firm*yearquarter FE
fitstat = ~ . + ivfall + ivwaldall + wh + wh.p,
tex = F)
## summary(fe_2sls, .. summary(fe_2sls_ro..
## Dependent Var.: RepIntensity RepIntensity
##
## AIA -0.0006*** (0.0001) -0.0005*** (0.0001)
## l(RepIntensity,1) 0.1758*** (0.0244) 0.1693*** (0.0241)
## ANews -0.0005*** (0.0002) -0.0006*** (0.0002)
## NewsAverage 0.0001 (0.0003) 0.0002 (0.0003)
## log(NewsCount) 0.0001 (0.0001) 0.0001 (0.0001)
## log(Amihud_monthly) -0.0021*** (0.0001) -0.0021*** (0.0002)
## OMRFlag 0.0017*** (0.0003) 0.0004 (0.0003)
## l(RET_m1,1) -0.0040*** (0.0005) -0.0038*** (0.0007)
## l(RET_m1,2) -0.0016*** (0.0004) -0.0014** (0.0005)
## l(RET_m1,3) -0.0006 (0.0004) -0.0005 (0.0004)
## log(TA) -0.0008* (0.0004) -0.0008. (0.0005)
## CtA 0.0018* (0.0008) 0.0020** (0.0007)
## EBITDAtA 0.0050. (0.0028) 0.0047. (0.0027)
## Leverage 0.0001 (0.0015) 0.0003 (0.0015)
## log(BM) 0.0009*** (0.0001) 0.0009*** (0.0001)
## DivtoAsset -0.0093 (0.0179) -0.0100 (0.0187)
## l(log(MarCap),1) -0.0012** (0.0004) -0.0013** (0.0004)
## log(ProgramMonth) -0.0005*** (6.56e-5)
## ShortInterest -0.0010 (0.0010)
## d(ShortInterest,1) 0.0179** (0.0061)
## Fixed-Effects: ------------------- --------------------
## Ticker Yes Yes
## time Yes Yes
## Ticker-year(YM) No No
## sic No No
## time:sic No No
## Ticker-yearqtr(YM) No No
## ___________________ ___________________ ____________________
## S.E.: Clustered by: Ticker by: Ticker & time
## Observations 39,470 39,106
## R2 0.25698 0.26110
## Within R2 0.05686 0.06229
## F-test (IV only) 49.443 45.356
## Wald (IV only) 26.339 20.072
## Wu-Hausman 2.0885 2.1235
## Wu-Hausman, p-value 0.14842 0.14506
##
## summary(fe_2sls_r.. summary(fe_2sls_r...1
## Dependent Var.: RepIntensity RepIntensity
##
## AIA -0.0005*** (0.0001) -0.0006*** (0.0002)
## l(RepIntensity,1) -0.0504* (0.0218) 0.1555*** (0.0383)
## ANews -0.0004* (0.0002) -0.0004 (0.0003)
## NewsAverage -0.0002 (0.0004) 0.0003 (0.0005)
## log(NewsCount) 0.0001 (0.0001) 8.87e-5 (0.0002)
## log(Amihud_monthly) -0.0023*** (0.0002) -0.0023*** (0.0003)
## OMRFlag -0.0002 (0.0004) 0.0002 (0.0006)
## l(RET_m1,1) -0.0036*** (0.0007) -0.0038** (0.0012)
## l(RET_m1,2) -0.0025*** (0.0007) -0.0014 (0.0009)
## l(RET_m1,3) -0.0013** (0.0004) 6.3e-5 (0.0008)
## log(TA) -0.0011 (0.0016) 0.0001 (0.0009)
## CtA 0.0088*** (0.0020) 0.0020. (0.0012)
## EBITDAtA -0.0036 (0.0038) 0.0003 (0.0035)
## Leverage 0.0028 (0.0044) -0.0020 (0.0027)
## log(BM) 0.0025*** (0.0005) 0.0004 (0.0003)
## DivtoAsset -0.0260 (0.0241) -0.0853 (0.1243)
## l(log(MarCap),1) 0.0008 (0.0008) -0.0027*** (0.0007)
## log(ProgramMonth) -0.0005*** (0.0001) -0.0004*** (0.0001)
## ShortInterest -0.0039 (0.0031) -0.0024 (0.0018)
## d(ShortInterest,1) 0.0173* (0.0071) 0.0238** (0.0077)
## Fixed-Effects: ------------------- -------------------
## Ticker Yes Yes
## time Yes Yes
## Ticker-year(YM) Yes No
## sic No Yes
## time:sic No Yes
## Ticker-yearqtr(YM) No No
## ___________________ ___________________ ___________________
## S.E.: Clustered by: Ticker & time by: Ticker & time
## Observations 39,106 39,106
## R2 0.42850 0.59901
## Within R2 0.02636 0.05264
## F-test (IV only) 36.062 29.514
## Wald (IV only) 19.846 11.979
## Wu-Hausman 1.0921 2.2160
## Wu-Hausman, p-value 0.29600 0.13661
##
## summary(fe_2sls_r...2
## Dependent Var.: RepIntensity
##
## AIA -0.0004** (0.0001)
## l(RepIntensity,1) -0.2415*** (0.0353)
## ANews -0.0004* (0.0002)
## NewsAverage -0.0002 (0.0004)
## log(NewsCount) 3.74e-5 (0.0001)
## log(Amihud_monthly) -0.0023*** (0.0002)
## OMRFlag -0.0012* (0.0006)
## l(RET_m1,1) -0.0027*** (0.0007)
## l(RET_m1,2) -0.0030*** (0.0008)
## l(RET_m1,3) -0.0016** (0.0006)
## log(TA) 0.0014 (0.0027)
## CtA 0.0165*** (0.0039)
## EBITDAtA -0.0219*** (0.0057)
## Leverage -0.0110 (0.0071)
## log(BM) 0.0071*** (0.0012)
## DivtoAsset
## l(log(MarCap),1) 0.0011 (0.0010)
## log(ProgramMonth) -0.0007** (0.0002)
## ShortInterest 0.0009 (0.0073)
## d(ShortInterest,1) 0.0102 (0.0088)
## Fixed-Effects: -------------------
## Ticker Yes
## time Yes
## Ticker-year(YM) No
## sic No
## time:sic No
## Ticker-yearqtr(YM) Yes
## ___________________ ___________________
## S.E.: Clustered by: Ticker & time
## Observations 39,106
## R2 0.60071
## Within R2 0.07732
## F-test (IV only) 18.399
## Wald (IV only) 7.2165
## Wu-Hausman --
## Wu-Hausman, p-value --
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
feols(fml = RepIntensity ~ l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + time + sic*time | AIA ~ MarketAIA, # # + Ticker^YQ #
data = pdShaRep, #[pdShaRep$Year >= 2012 & pdShaRep$Year < 2020,], # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker", "time")) # "Ticker^time"
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679, Fixed-effects: 1,243).
## TSLS estimation, Dep. Var.: RepIntensity, Endo.: AIA, Instr.: MarketAIA
## Second stage: Dep. Var.: RepIntensity
## Observations: 39,106
## Fixed-effects: Ticker: 1,216, time: 102, sic: 293, time:sic: 17,417
## Standard-errors: Clustered (Ticker & time)
## Estimate Std. Error t value Pr(>|t|)
## fit_AIA -0.000614 0.000177 -3.461116 7.8976e-04 ***
## l(RepIntensity, 1) 0.155455 0.038265 4.062628 9.6049e-05 ***
## ANews -0.000375 0.000271 -1.383654 1.6951e-01
## NewsAverage 0.000333 0.000509 0.655675 5.1352e-01
## log(NewsCount) 0.000089 0.000187 0.474077 6.3647e-01
## log(Amihud_monthly) -0.002309 0.000316 -7.318063 6.1787e-11 ***
## OMRFlag 0.000238 0.000557 0.427420 6.6998e-01
## l(RET_m1, 1) -0.003848 0.001159 -3.319974 1.2533e-03 **
## l(RET_m1, 2) -0.001418 0.000881 -1.609213 1.1069e-01
## l(RET_m1, 3) 0.000063 0.000806 0.078239 9.3779e-01
## log(TA) 0.000122 0.000850 0.143953 8.8582e-01
## CtA 0.001969 0.001152 1.709037 9.0515e-02 .
## EBITDAtA 0.000288 0.003516 0.081774 9.3499e-01
## Leverage -0.002031 0.002741 -0.741168 4.6031e-01
## log(BM) 0.000388 0.000286 1.357704 1.7758e-01
## DivtoAsset -0.085269 0.124343 -0.685751 4.9444e-01
## log(ProgramMonth) -0.000434 0.000106 -4.085169 8.8390e-05 ***
## l(log(MarCap), 1) -0.002703 0.000695 -3.891900 1.7855e-04 ***
## ShortInterest -0.002359 0.001765 -1.336608 1.8435e-01
## d(ShortInterest, 1) 0.023761 0.007694 3.088286 2.5998e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.00398 Adj. R2: 0.218354
## Within R2: 0.052638
## F-test (1st stage), AIA: stat = 81,553.3 , p < 2.2e-16 , on 1 and 21,377 DoF.
## Wu-Hausman: stat = 2.21596, p = 0.136605, on 1 and 20,060 DoF.
# the concern about this captures some macro shocks: the idea is that macro shocks (e.g. covid) will simultaneously impact
# the attention to the firm, which should be low, and drop the firm price, might be undervalued, and thus lead to more repurchases
# simply before and after the
cat("\n")
cat("From this we can see the treatment effect persists within each year and are generally statistically significant. ")
## From this we can see the treatment effect persists within each year and are generally statistically significant.
feols(fml = RepIntensity ~ AIA:i(Year) + l(RepIntensity, 1) +
# l(ANews, 0) + l(log(1+NewsAverage), 0) + l(log(1+NewsCount), 0) +
ANews + NewsAverage + log(NewsCount) +
log(Amihud_monthly) + OMRFlag + # log(1 + NewsCount) +
# RET_m1 + TA + CtA + (EBITDA/TA) + Leverage + BM + DivtoAsset +
# MarCap + Month + Volatility + TradeVol
l(RET_m1, 1:3)+ log(TA) + (CtA) + (EBITDAtA) + (Leverage) + log(BM) + (DivtoAsset) + log(ProgramMonth) +
l(log(MarCap), 1) + ShortInterest + d(ShortInterest,1) # + d(Volatility,1) + l(Volatility, 1) + TradeVol_scaled # + lambda
| Ticker + Year + sic*Year, # # + Ticker^YQ #
data = pdShaRep, # [pdShaRep$Year !=2011 & pdShaRep$Year != 2020,], # [pdShaRep$AIA < 4.0,], # [pdShaRep$RepIntensity > 0,],
cluster = c("Ticker")) %>% # "Ticker^time"
summary()
## Warning in log(BM): NaNs produced
## NOTE: 34,679 observations removed because of NA and infinite values (RHS: 34,679, Fixed-effects: 1,243).
## OLS estimation, Dep. Var.: RepIntensity
## Observations: 39,106
## Fixed-effects: Ticker: 1,216, Year: 12, sic: 293, Year:sic: 2,432
## Standard-errors: Clustered (Ticker)
## Estimate Std. Error t value Pr(>|t|)
## l(RepIntensity, 1) 0.082799 0.022819 3.628595 2.9680e-04 ***
## ANews -0.000649 0.000160 -4.049321 5.4619e-05 ***
## NewsAverage -0.000312 0.000370 -0.841527 4.0022e-01
## log(NewsCount) 0.000271 0.000101 2.687651 7.2939e-03 **
## log(Amihud_monthly) -0.001105 0.000131 -8.406160 < 2.2e-16 ***
## OMRFlag 0.000428 0.000375 1.142493 2.5347e-01
## l(RET_m1, 1) -0.003406 0.000446 -7.644152 4.2529e-14 ***
## l(RET_m1, 2) -0.001863 0.000401 -4.642619 3.8139e-06 ***
## l(RET_m1, 3) -0.001378 0.000336 -4.108564 4.2480e-05 ***
## log(TA) -0.002291 0.000563 -4.068234 5.0425e-05 ***
## CtA 0.002908 0.000938 3.099704 1.9817e-03 **
## EBITDAtA 0.000575 0.002956 0.194319 8.4596e-01
## Leverage 0.006517 0.001836 3.549882 4.0013e-04 ***
## log(BM) 0.001227 0.000219 5.614876 2.4356e-08 ***
## DivtoAsset -0.010300 0.019984 -0.515423 6.0635e-01
## log(ProgramMonth) -0.000423 0.000077 -5.507998 4.4261e-08 ***
## l(log(MarCap), 1) 0.000947 0.000454 2.086879 3.7107e-02 *
## ShortInterest -0.000290 0.001384 -0.209388 8.3418e-01
## d(ShortInterest, 1) 0.019124 0.006836 2.797356 5.2336e-03 **
## AIA:Year::2010 -0.000614 0.000350 -1.757404 7.9101e-02 .
## AIA:Year::2011 -0.001192 0.000310 -3.844264 1.2718e-04 ***
## AIA:Year::2012 -0.000946 0.000238 -3.972030 7.5450e-05 ***
## AIA:Year::2013 -0.000669 0.000210 -3.178291 1.5188e-03 **
## AIA:Year::2014 -0.000726 0.000186 -3.910252 9.7299e-05 ***
## AIA:Year::2015 -0.000456 0.000195 -2.334344 1.9740e-02 *
## AIA:Year::2016 -0.000608 0.000200 -3.032028 2.4806e-03 **
## AIA:Year::2017 -0.000769 0.000152 -5.071516 4.5603e-07 ***
## AIA:Year::2018 -0.000679 0.000223 -3.043075 2.3920e-03 **
## AIA:Year::2019 -0.000171 0.000164 -1.041331 2.9793e-01
## AIA:Year::2020 -0.000611 0.000169 -3.623201 3.0299e-04 ***
## AIA:Year::2021 -0.000666 0.000229 -2.902728 3.7661e-03 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## RMSE: 0.005224 Adj. R2: 0.230918
## Within R2: 0.030115
# From this we can see the treatment effect persists within each year and are generally statistically significant.